In [1]:
#Import Packages to be used in script
import pandas as pd
import geopandas as gpd
import requests
import hvplot.pandas
import folium
import holoviews as hv
import matplotlib.pyplot as plt
hv.extension('bokeh', logo = False)
from bokeh.models.formatters import NumeralTickFormatter
In [2]:
#API Key from census.gov
#Rishabh Singh's user key: 
#(Put your own key if anyone else is using this script)
usr_key = '&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [3]:
pd.set_option("display.max_rows", 10)
In [4]:
Year = '2022' #Set which year you need the data for

Heat Sensitivity Variables: (Check mastersheet for explanations)¶

(Multiply by 100 for percents)¶

  1. % pop Below 150% Pov Line: B06012_002E + B06012_003E/B01003_001E
  2. % Severely Burdened Renter units: (B25106_028E + B25106_032E + B25106_036E + B25106_040E + B25106_044E)/B25106_001E
  3. % HU with Housing Cost Burden: (B25106_006E + B25106_010E + B25106_014E + B25106_018E + B25106_022E)/B25106_001E
  4. % Housing Units with No Vehicle: DP04_0058E/DP04_0057E
  5. % pop with no health insurance: DP03_0099E/DP05_0001E
  6. % pop with a disability: (B18101_004E + B18101_007E + B18101_010E + B18101_013E + B18101_016E + B18101_019E + B18101_023E + B18101_026E + B18101_029E + B18101_032E + B18101_035E + B18101_038E) /B01003_001E
  7. % pop aged 65 years and over: DP05_0024E/DP05_0001E
  8. % pop aged under 5 years: DP05_0005E/DP05_0001E
  9. % pop that speaks english less than "very well": B06007_005E + B06007_008E/B01003_001E
  10. % Housing units with more than 1 person per room out of total occupied housing units: (DP04_0078E + DP04_0079E)/DP04_0076E
  11. Communities of color: All underserved population: (B01001H_001E + B01001B_001E + B01001C_001E + B01001D_001E + B01001E_001E + B01001I_001E)/B01003_001E
  12. Pregnant Women: Using Proxy variable - % Women who had a birth in the past 12 months: B13002_002E/B01003_001E
  13. Other Health Variables - adults with hypertension, adults with COPD, adults with asthma, adults with diabetes, adults reported as obese, Mental health Issues; In PLACES Data. - Crude Prevalance rate.
  14. Homeless Population - PIT Data - Obtained by rating the point data 1 to 5 with 5 having most homeless people.
  15. Energy Burden

-> Variable for Total Population:

    - For Detailed Tables: B01003_001E
    - For Data Profiles: DP05_0001E

-> Variable for Total Housing Units:

    - For Detailed Tables: B25106_001E
    - For Data Profiles: DP04_0057E or DP04_0076E
In [5]:
## All variables data checked 
## Need to check all variable definitions before using once from data.census.gov
In [6]:
## Detailed Tables
In [7]:
## Var-1: % pop Below 100% Pov Line: B06012_003E/B06012_001E*100
In [8]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5'
get = '?get='
variables = 'B06012_002E,B06012_003E,B01003_001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [9]:
query_url_HC_YM_acs2021
Out[9]:
'https://api.census.gov/data/2022/acs/acs5?get=B06012_002E,B06012_003E,B01003_001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [10]:
response_HC_YM_acs2021
Out[10]:
<Response [200]>
In [11]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[11]:
B06012_002E B06012_003E B01003_001E state county tract
1 393 168 2722 51 036 600100
2 240 137 2374 51 036 600200
3 203 181 1664 51 036 600300
4 86 102 3851 51 041 100106
5 860 159 2511 51 041 100107
... ... ... ... ... ... ...
292 408 758 3092 51 760 070902
293 572 101 4257 51 760 071002
294 514 131 2556 51 760 071003
295 456 419 1821 51 760 071004
296 1121 256 5380 51 760 071100

296 rows × 6 columns

In [12]:
#Getting Ashland Data-
In [13]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [14]:
df_acs2021_a # Ashland Census Tracts
Out[14]:
B06012_002E B06012_003E B01003_001E state county tract
90 840 165 5249 51 03368 320601
91 200 61 2952 51 03368 320602
In [15]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[15]:
B06012_002E B06012_003E B01003_001E state county tract
1 393 168 2722 51 036 600100
2 240 137 2374 51 036 600200
3 203 181 1664 51 036 600300
4 86 102 3851 51 041 100106
5 860 159 2511 51 041 100107
... ... ... ... ... ... ...
294 514 131 2556 51 760 071003
295 456 419 1821 51 760 071004
296 1121 256 5380 51 760 071100
90 840 165 5249 51 03368 320601
91 200 61 2952 51 03368 320602

298 rows × 6 columns

In [16]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [17]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [18]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [19]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [20]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [21]:
df_acs2021_comb
Out[21]:
B06012_002E B06012_003E B01003_001E state county tract
0 200 61 2952 51 03368 320602
1 840 165 5249 51 03368 320601
2 393 168 2722 51 036 600100
3 240 137 2374 51 036 600200
4 203 181 1664 51 036 600300
... ... ... ... ... ... ...
293 668 338 10555 51 095 080402
294 108 120 3919 51 095 080403
295 289 34 4168 51 095 080404
296 200 206 3725 51 095 080208
297 181 209 4411 51 095 080308

298 rows × 6 columns

In [22]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[22]:
Int64Index([105, 110], dtype='int64')
In [23]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[23]:
B06012_002E B06012_003E B01003_001E state county tract
0 200 61 2952 51 03368 320602
1 840 165 5249 51 03368 320601
2 393 168 2722 51 036 600100
3 240 137 2374 51 036 600200
4 203 181 1664 51 036 600300
... ... ... ... ... ... ...
293 668 338 10555 51 095 080402
294 108 120 3919 51 095 080403
295 289 34 4168 51 095 080404
296 200 206 3725 51 095 080208
297 181 209 4411 51 095 080308

296 rows × 6 columns

In [24]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [25]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[25]:
B06012_002E B06012_003E B01003_001E state county tract GEOID
0 200 61 2952 51 03368 320602 5103368320602
1 840 165 5249 51 03368 320601 5103368320601
2 393 168 2722 51 036 600100 51036600100
3 240 137 2374 51 036 600200 51036600200
4 203 181 1664 51 036 600300 51036600300
... ... ... ... ... ... ... ...
293 668 338 10555 51 095 080402 51095080402
294 108 120 3919 51 095 080403 51095080403
295 289 34 4168 51 095 080404 51095080404
296 200 206 3725 51 095 080208 51095080208
297 181 209 4411 51 095 080308 51095080308

296 rows × 7 columns

In [26]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[26]:
B06012_002E B06012_003E B01003_001E state county tract GEOID
0 200 61 2952 51 03368 320602 51085320602
1 840 165 5249 51 03368 320601 51085320601
2 393 168 2722 51 036 600100 51036600100
3 240 137 2374 51 036 600200 51036600200
4 203 181 1664 51 036 600300 51036600300
... ... ... ... ... ... ... ...
293 668 338 10555 51 095 080402 51095080402
294 108 120 3919 51 095 080403 51095080403
295 289 34 4168 51 095 080404 51095080404
296 200 206 3725 51 095 080208 51095080208
297 181 209 4411 51 095 080308 51095080308

296 rows × 7 columns

In [27]:
# Rename column headers
df_acs2021['Pop_pov'] = df_acs2021['B06012_002E'].astype(int) + df_acs2021['B06012_003E'].astype(int)
df_acs2021.rename (columns = {'B01003_001E':'Tot_pop'}, inplace = True)
df_acs2021.head()
Out[27]:
B06012_002E B06012_003E Tot_pop state county tract GEOID Pop_pov
0 200 61 2952 51 03368 320602 51085320602 261
1 840 165 5249 51 03368 320601 51085320601 1005
2 393 168 2722 51 036 600100 51036600100 561
3 240 137 2374 51 036 600200 51036600200 377
4 203 181 1664 51 036 600300 51036600300 384
In [28]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[28]:
B06012_002E B06012_003E Tot_pop state county tract GEOID Pop_pov Year
0 200 61 2952 51 03368 320602 51085320602 261 2022
1 840 165 5249 51 03368 320601 51085320601 1005 2022
2 393 168 2722 51 036 600100 51036600100 561 2022
3 240 137 2374 51 036 600200 51036600200 377 2022
4 203 181 1664 51 036 600300 51036600300 384 2022
In [29]:
df_acs2021['Pop_pov'] = df_acs2021['Pop_pov'].astype(int)
df_acs2021['Tot_pop'] = df_acs2021['Tot_pop'].astype(int)
df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[29]:
B06012_002E B06012_003E Tot_pop state county tract GEOID Pop_pov Year
0 200 61 2952 51 03368 320602 51085320602 261 2022
1 840 165 5249 51 03368 320601 51085320601 1005 2022
2 393 168 2722 51 036 600100 51036600100 561 2022
3 240 137 2374 51 036 600200 51036600200 377 2022
4 203 181 1664 51 036 600300 51036600300 384 2022
... ... ... ... ... ... ... ... ... ...
293 668 338 10555 51 095 080402 51095080402 1006 2022
294 108 120 3919 51 095 080403 51095080403 228 2022
295 289 34 4168 51 095 080404 51095080404 323 2022
296 200 206 3725 51 095 080208 51095080208 406 2022
297 181 209 4411 51 095 080308 51095080308 390 2022

296 rows × 9 columns

In [30]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [31]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [32]:
df_acs2021.columns
Out[32]:
Index(['B06012_002E', 'B06012_003E', 'Tot_pop', 'state', 'county', 'tract',
       'GEOID', 'Pop_pov', 'Year', 'locality'],
      dtype='object')
In [33]:
df_acs2021
Out[33]:
B06012_002E B06012_003E Tot_pop state county tract GEOID Pop_pov Year locality
0 200 61 2952 51 03368 320602 51085320602 261 2022 Ashland
1 840 165 5249 51 03368 320601 51085320601 1005 2022 Ashland
2 393 168 2722 51 036 600100 51036600100 561 2022 Charles City
3 240 137 2374 51 036 600200 51036600200 377 2022 Charles City
4 203 181 1664 51 036 600300 51036600300 384 2022 Charles City
... ... ... ... ... ... ... ... ... ... ...
293 668 338 10555 51 095 080402 51095080402 1006 2022 James City
294 108 120 3919 51 095 080403 51095080403 228 2022 James City
295 289 34 4168 51 095 080404 51095080404 323 2022 James City
296 200 206 3725 51 095 080208 51095080208 406 2022 James City
297 181 209 4411 51 095 080308 51095080308 390 2022 James City

296 rows × 10 columns

In [34]:
df_acs2021['Perc_pov'] = (df_acs2021['Pop_pov']/df_acs2021['Tot_pop'])*100
In [35]:
df_acs2021
Out[35]:
B06012_002E B06012_003E Tot_pop state county tract GEOID Pop_pov Year locality Perc_pov
0 200 61 2952 51 03368 320602 51085320602 261 2022 Ashland 8.841463
1 840 165 5249 51 03368 320601 51085320601 1005 2022 Ashland 19.146504
2 393 168 2722 51 036 600100 51036600100 561 2022 Charles City 20.609846
3 240 137 2374 51 036 600200 51036600200 377 2022 Charles City 15.880371
4 203 181 1664 51 036 600300 51036600300 384 2022 Charles City 23.076923
... ... ... ... ... ... ... ... ... ... ... ...
293 668 338 10555 51 095 080402 51095080402 1006 2022 James City 9.531028
294 108 120 3919 51 095 080403 51095080403 228 2022 James City 5.817811
295 289 34 4168 51 095 080404 51095080404 323 2022 James City 7.749520
296 200 206 3725 51 095 080208 51095080208 406 2022 James City 10.899329
297 181 209 4411 51 095 080308 51095080308 390 2022 James City 8.841533

296 rows × 11 columns

In [36]:
## Store vaariable data in new variable 

df_acs2021_var1 = df_acs2021
In [37]:
## Visualize on a map
In [38]:
gdf_US_county = gpd.read_file(r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\DataHub-Rishabh\Default Shapefiles\us_county\tl_rd22_us_county.shp")
In [39]:
gdf_US_county.head()
Out[39]:
STATEFP COUNTYFP COUNTYNS GEOID NAME NAMELSAD LSAD CLASSFP MTFCC CSAFP CBSAFP METDIVFP FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry
0 31 039 00835841 31039 Cuming Cuming County 06 H1 G4020 NaN NaN NaN A 1477645345 10690204 +41.9158651 -096.7885168 POLYGON ((-97.01952 42.00410, -97.01952 42.004...
1 53 069 01513275 53069 Wahkiakum Wahkiakum County 06 H1 G4020 NaN NaN NaN A 680976231 61568965 +46.2946377 -123.4244583 POLYGON ((-123.43639 46.23820, -123.44759 46.2...
2 35 011 00933054 35011 De Baca De Baca County 06 H1 G4020 NaN NaN NaN A 6016818946 29090018 +34.3592729 -104.3686961 POLYGON ((-104.56739 33.99757, -104.56772 33.9...
3 31 109 00835876 31109 Lancaster Lancaster County 06 H1 G4020 339 30700 NaN A 2169272970 22847034 +40.7835474 -096.6886584 POLYGON ((-96.91075 40.78494, -96.91075 40.790...
4 31 129 00835886 31129 Nuckolls Nuckolls County 06 H1 G4020 NaN NaN NaN A 1489645188 1718484 +40.1764918 -098.0468422 POLYGON ((-98.27367 40.08940, -98.27367 40.089...
In [40]:
gdf_County_RVA=gdf_US_county[(gdf_US_county['GEOID'] == '51036')|(gdf_US_county['GEOID'] == '51041')|(gdf_US_county['GEOID'] == '51075')|(gdf_US_county['GEOID'] == '51085')|(gdf_US_county['GEOID'] == '51087')|(gdf_US_county['GEOID'] == '51127')|(gdf_US_county['GEOID'] == '51145')|(gdf_US_county['GEOID'] == '51760')|(gdf_US_county['GEOID'] == '51095')]
In [41]:
gdf_US_CT = gpd.read_file(r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\DataHub-Rishabh\Default Shapefiles\us_censustracts\tl_rd22_51_tract.shp")
In [42]:
gdf_US_CT.head()
Out[42]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874...
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607...
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595...
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 +37.7231726 -077.5209363 POLYGON ((-77.61233 37.75986, -77.61216 37.760...
4 51 810 045000 51810045000 450 Census Tract 450 G5020 S 21420965 29350 +36.8180371 -076.0308570 POLYGON ((-76.06314 36.80147, -76.06114 36.802...
In [43]:
gdf_perc_pov = pd.merge(gdf_US_CT,df_acs2021_var1, on = 'GEOID', how = 'inner')
In [44]:
gdf_perc_pov.head()
Out[44]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... B06012_002E B06012_003E Tot_pop state county tract Pop_pov Year locality Perc_pov
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... 115 210 5516 51 085 320100 325 2022 Hanover 5.891951
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... 183 295 4585 51 085 321201 478 2022 Hanover 10.425300
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... 692 724 4364 51 085 321202 1416 2022 Hanover 32.447296
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... 361 502 5455 51 085 320400 863 2022 Hanover 15.820348
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... 826 405 3238 51 087 201002 1231 2022 Henrico 38.017295

5 rows × 23 columns

In [45]:
import os

# Define the directory where you want to save the maps
save_dir = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\Plots"
In [46]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_perc_pov.plot(column='Perc_pov',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Poverty Percentage by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
# Add text box
textstr = "Percent population at or above 150 % poverty level"
props = dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')
ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=14,
        verticalalignment='top', horizontalalignment='right', bbox=props)

# Save the map
map_name = 'Perc_pov.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [47]:
df_acs2021_var1
Out[47]:
B06012_002E B06012_003E Tot_pop state county tract GEOID Pop_pov Year locality Perc_pov
0 200 61 2952 51 03368 320602 51085320602 261 2022 Ashland 8.841463
1 840 165 5249 51 03368 320601 51085320601 1005 2022 Ashland 19.146504
2 393 168 2722 51 036 600100 51036600100 561 2022 Charles City 20.609846
3 240 137 2374 51 036 600200 51036600200 377 2022 Charles City 15.880371
4 203 181 1664 51 036 600300 51036600300 384 2022 Charles City 23.076923
... ... ... ... ... ... ... ... ... ... ... ...
293 668 338 10555 51 095 080402 51095080402 1006 2022 James City 9.531028
294 108 120 3919 51 095 080403 51095080403 228 2022 James City 5.817811
295 289 34 4168 51 095 080404 51095080404 323 2022 James City 7.749520
296 200 206 3725 51 095 080208 51095080208 406 2022 James City 10.899329
297 181 209 4411 51 095 080308 51095080308 390 2022 James City 8.841533

296 rows × 11 columns

In [ ]:
In [48]:
## Var-2: % Severely Burdened Renter units: (B25106_028E + B25106_032E + B25106_036E + B25106_040E + B25106_044E)/B25106_001E
In [49]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5'
get = '?get='
variables = 'B25106_028E,B25106_032E,B25106_036E,B25106_040E,B25106_044E,B25106_001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [50]:
query_url_HC_YM_acs2021
Out[50]:
'https://api.census.gov/data/2022/acs/acs5?get=B25106_028E,B25106_032E,B25106_036E,B25106_040E,B25106_044E,B25106_001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [51]:
response_HC_YM_acs2021
Out[51]:
<Response [200]>
In [52]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[52]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract
1 50 4 7 8 0 1147 51 036 600100
2 6 24 0 2 0 990 51 036 600200
3 44 6 0 8 0 768 51 036 600300
4 0 0 0 0 0 1421 51 041 100106
5 146 196 116 62 0 1087 51 041 100107
... ... ... ... ... ... ... ... ... ...
292 103 216 143 0 0 1116 51 760 070902
293 178 159 58 16 0 1519 51 760 071002
294 122 167 332 0 0 1491 51 760 071003
295 112 122 36 70 0 829 51 760 071004
296 331 409 164 92 0 2959 51 760 071100

296 rows × 9 columns

In [53]:
#Getting Ashland Data-
In [54]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [55]:
df_acs2021_a # Ashland Census Tracts
Out[55]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract
90 145 186 29 39 0 1433 51 03368 320601
91 24 13 16 12 45 1445 51 03368 320602
In [56]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[56]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract
1 50 4 7 8 0 1147 51 036 600100
2 6 24 0 2 0 990 51 036 600200
3 44 6 0 8 0 768 51 036 600300
4 0 0 0 0 0 1421 51 041 100106
5 146 196 116 62 0 1087 51 041 100107
... ... ... ... ... ... ... ... ... ...
294 122 167 332 0 0 1491 51 760 071003
295 112 122 36 70 0 829 51 760 071004
296 331 409 164 92 0 2959 51 760 071100
90 145 186 29 39 0 1433 51 03368 320601
91 24 13 16 12 45 1445 51 03368 320602

298 rows × 9 columns

In [57]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [58]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [59]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [60]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [61]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [62]:
df_acs2021_comb
Out[62]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract
0 24 13 16 12 45 1445 51 03368 320602
1 145 186 29 39 0 1433 51 03368 320601
2 50 4 7 8 0 1147 51 036 600100
3 6 24 0 2 0 990 51 036 600200
4 44 6 0 8 0 768 51 036 600300
... ... ... ... ... ... ... ... ... ...
293 73 63 63 0 0 3818 51 095 080402
294 19 0 0 0 0 1552 51 095 080403
295 7 0 0 0 26 1458 51 095 080404
296 23 0 24 0 0 1448 51 095 080208
297 0 76 23 0 26 1637 51 095 080308

298 rows × 9 columns

In [63]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[63]:
Int64Index([105, 110], dtype='int64')
In [64]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[64]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract
0 24 13 16 12 45 1445 51 03368 320602
1 145 186 29 39 0 1433 51 03368 320601
2 50 4 7 8 0 1147 51 036 600100
3 6 24 0 2 0 990 51 036 600200
4 44 6 0 8 0 768 51 036 600300
... ... ... ... ... ... ... ... ... ...
293 73 63 63 0 0 3818 51 095 080402
294 19 0 0 0 0 1552 51 095 080403
295 7 0 0 0 26 1458 51 095 080404
296 23 0 24 0 0 1448 51 095 080208
297 0 76 23 0 26 1637 51 095 080308

296 rows × 9 columns

In [65]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [66]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[66]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract GEOID
0 24 13 16 12 45 1445 51 03368 320602 5103368320602
1 145 186 29 39 0 1433 51 03368 320601 5103368320601
2 50 4 7 8 0 1147 51 036 600100 51036600100
3 6 24 0 2 0 990 51 036 600200 51036600200
4 44 6 0 8 0 768 51 036 600300 51036600300
... ... ... ... ... ... ... ... ... ... ...
293 73 63 63 0 0 3818 51 095 080402 51095080402
294 19 0 0 0 0 1552 51 095 080403 51095080403
295 7 0 0 0 26 1458 51 095 080404 51095080404
296 23 0 24 0 0 1448 51 095 080208 51095080208
297 0 76 23 0 26 1637 51 095 080308 51095080308

296 rows × 10 columns

In [67]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[67]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract GEOID
0 24 13 16 12 45 1445 51 03368 320602 51085320602
1 145 186 29 39 0 1433 51 03368 320601 51085320601
2 50 4 7 8 0 1147 51 036 600100 51036600100
3 6 24 0 2 0 990 51 036 600200 51036600200
4 44 6 0 8 0 768 51 036 600300 51036600300
... ... ... ... ... ... ... ... ... ... ...
293 73 63 63 0 0 3818 51 095 080402 51095080402
294 19 0 0 0 0 1552 51 095 080403 51095080403
295 7 0 0 0 26 1458 51 095 080404 51095080404
296 23 0 24 0 0 1448 51 095 080208 51095080208
297 0 76 23 0 26 1637 51 095 080308 51095080308

296 rows × 10 columns

In [68]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[68]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract GEOID Year
0 24 13 16 12 45 1445 51 03368 320602 51085320602 2022
1 145 186 29 39 0 1433 51 03368 320601 51085320601 2022
2 50 4 7 8 0 1147 51 036 600100 51036600100 2022
3 6 24 0 2 0 990 51 036 600200 51036600200 2022
4 44 6 0 8 0 768 51 036 600300 51036600300 2022
In [69]:
df_acs2021['B25106_028E'] = df_acs2021['B25106_028E'].astype(int)
df_acs2021['B25106_032E'] = df_acs2021['B25106_032E'].astype(int)
df_acs2021['B25106_036E'] = df_acs2021['B25106_036E'].astype(int)
df_acs2021['B25106_040E'] = df_acs2021['B25106_040E'].astype(int)
df_acs2021['B25106_044E'] = df_acs2021['B25106_044E'].astype(int)
df_acs2021['B25106_001E'] = df_acs2021['B25106_001E'].astype(int)
df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[69]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract GEOID Year
0 24 13 16 12 45 1445 51 03368 320602 51085320602 2022
1 145 186 29 39 0 1433 51 03368 320601 51085320601 2022
2 50 4 7 8 0 1147 51 036 600100 51036600100 2022
3 6 24 0 2 0 990 51 036 600200 51036600200 2022
4 44 6 0 8 0 768 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ... ... ... ... ...
293 73 63 63 0 0 3818 51 095 080402 51095080402 2022
294 19 0 0 0 0 1552 51 095 080403 51095080403 2022
295 7 0 0 0 26 1458 51 095 080404 51095080404 2022
296 23 0 24 0 0 1448 51 095 080208 51095080208 2022
297 0 76 23 0 26 1637 51 095 080308 51095080308 2022

296 rows × 11 columns

In [70]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [71]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [72]:
df_acs2021.columns
Out[72]:
Index(['B25106_028E', 'B25106_032E', 'B25106_036E', 'B25106_040E',
       'B25106_044E', 'B25106_001E', 'state', 'county', 'tract', 'GEOID',
       'Year', 'locality'],
      dtype='object')
In [73]:
df_acs2021
Out[73]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract GEOID Year locality
0 24 13 16 12 45 1445 51 03368 320602 51085320602 2022 Ashland
1 145 186 29 39 0 1433 51 03368 320601 51085320601 2022 Ashland
2 50 4 7 8 0 1147 51 036 600100 51036600100 2022 Charles City
3 6 24 0 2 0 990 51 036 600200 51036600200 2022 Charles City
4 44 6 0 8 0 768 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ... ... ... ... ...
293 73 63 63 0 0 3818 51 095 080402 51095080402 2022 James City
294 19 0 0 0 0 1552 51 095 080403 51095080403 2022 James City
295 7 0 0 0 26 1458 51 095 080404 51095080404 2022 James City
296 23 0 24 0 0 1448 51 095 080208 51095080208 2022 James City
297 0 76 23 0 26 1637 51 095 080308 51095080308 2022 James City

296 rows × 12 columns

In [74]:
df_acs2021['Perc_BurdRU'] = ((df_acs2021['B25106_028E']+df_acs2021['B25106_032E']+df_acs2021['B25106_036E']+df_acs2021['B25106_040E']+df_acs2021['B25106_044E'])/df_acs2021['B25106_001E'])*100
In [75]:
df_acs2021
Out[75]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract GEOID Year locality Perc_BurdRU
0 24 13 16 12 45 1445 51 03368 320602 51085320602 2022 Ashland 7.612457
1 145 186 29 39 0 1433 51 03368 320601 51085320601 2022 Ashland 27.843685
2 50 4 7 8 0 1147 51 036 600100 51036600100 2022 Charles City 6.015693
3 6 24 0 2 0 990 51 036 600200 51036600200 2022 Charles City 3.232323
4 44 6 0 8 0 768 51 036 600300 51036600300 2022 Charles City 7.552083
... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 73 63 63 0 0 3818 51 095 080402 51095080402 2022 James City 5.212153
294 19 0 0 0 0 1552 51 095 080403 51095080403 2022 James City 1.224227
295 7 0 0 0 26 1458 51 095 080404 51095080404 2022 James City 2.263374
296 23 0 24 0 0 1448 51 095 080208 51095080208 2022 James City 3.245856
297 0 76 23 0 26 1637 51 095 080308 51095080308 2022 James City 7.635919

296 rows × 13 columns

In [76]:
## Store vaariable data in new variable 

df_acs2021_var2 = df_acs2021
In [77]:
## Visualize on a map
In [78]:
gdf_BurdRU = pd.merge(gdf_US_CT,df_acs2021_var2, on = 'GEOID', how = 'inner')
In [79]:
gdf_BurdRU.head()
Out[79]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... B25106_036E B25106_040E B25106_044E B25106_001E state county tract Year locality Perc_BurdRU
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... 61 0 0 2215 51 085 320100 2022 Hanover 2.753950
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... 73 0 0 1887 51 085 321201 2022 Hanover 8.373079
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... 89 112 0 1778 51 085 321202 2022 Hanover 37.851519
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... 15 21 0 2163 51 085 320400 2022 Hanover 7.628294
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... 44 75 0 1248 51 087 201002 2022 Henrico 27.724359

5 rows × 25 columns

In [80]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_BurdRU.plot(column='Perc_BurdRU',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Severely Burdened Renter units by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
# Add text box
textstr = "Severely Burdened Renter Unit: % of household salary required for Rent > 30%"
props = dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')
ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=14,
        verticalalignment='top', horizontalalignment='right', bbox=props)

# Save the map
map_name = 'Perc_BurdRU.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [81]:
df_acs2021_var2
Out[81]:
B25106_028E B25106_032E B25106_036E B25106_040E B25106_044E B25106_001E state county tract GEOID Year locality Perc_BurdRU
0 24 13 16 12 45 1445 51 03368 320602 51085320602 2022 Ashland 7.612457
1 145 186 29 39 0 1433 51 03368 320601 51085320601 2022 Ashland 27.843685
2 50 4 7 8 0 1147 51 036 600100 51036600100 2022 Charles City 6.015693
3 6 24 0 2 0 990 51 036 600200 51036600200 2022 Charles City 3.232323
4 44 6 0 8 0 768 51 036 600300 51036600300 2022 Charles City 7.552083
... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 73 63 63 0 0 3818 51 095 080402 51095080402 2022 James City 5.212153
294 19 0 0 0 0 1552 51 095 080403 51095080403 2022 James City 1.224227
295 7 0 0 0 26 1458 51 095 080404 51095080404 2022 James City 2.263374
296 23 0 24 0 0 1448 51 095 080208 51095080208 2022 James City 3.245856
297 0 76 23 0 26 1637 51 095 080308 51095080308 2022 James City 7.635919

296 rows × 13 columns

In [ ]:
In [82]:
## % HU with Housing Cost Burden: (B25106_006E + B25106_010E + B25106_014E + B25106_018E + B25106_022E)/B25106_001E
In [83]:
## Var-3: % HU with Housing Cost Burden: (B25106_006E + B25106_010E + B25106_014E + B25106_018E + B25106_022E)/B25106_001E
In [84]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5'
get = '?get='
variables = 'B25106_006E,B25106_010E,B25106_014E,B25106_018E,B25106_022E,B25106_001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [85]:
query_url_HC_YM_acs2021
Out[85]:
'https://api.census.gov/data/2022/acs/acs5?get=B25106_006E,B25106_010E,B25106_014E,B25106_018E,B25106_022E,B25106_001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [86]:
response_HC_YM_acs2021
Out[86]:
<Response [200]>
In [87]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[87]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract
1 107 43 18 28 11 1147 51 036 600100
2 65 20 36 23 1 990 51 036 600200
3 47 13 45 3 0 768 51 036 600300
4 14 51 30 76 16 1421 51 041 100106
5 32 55 9 6 0 1087 51 041 100107
... ... ... ... ... ... ... ... ... ...
292 10 0 0 0 56 1116 51 760 070902
293 62 14 36 38 12 1519 51 760 071002
294 45 23 45 7 0 1491 51 760 071003
295 0 0 0 0 0 829 51 760 071004
296 24 51 38 31 57 2959 51 760 071100

296 rows × 9 columns

In [88]:
#Getting Ashland Data-
In [89]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [90]:
df_acs2021_a # Ashland Census Tracts
Out[90]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract
90 45 5 0 27 6 1433 51 03368 320601
91 37 114 17 26 0 1445 51 03368 320602
In [91]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[91]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract
1 107 43 18 28 11 1147 51 036 600100
2 65 20 36 23 1 990 51 036 600200
3 47 13 45 3 0 768 51 036 600300
4 14 51 30 76 16 1421 51 041 100106
5 32 55 9 6 0 1087 51 041 100107
... ... ... ... ... ... ... ... ... ...
294 45 23 45 7 0 1491 51 760 071003
295 0 0 0 0 0 829 51 760 071004
296 24 51 38 31 57 2959 51 760 071100
90 45 5 0 27 6 1433 51 03368 320601
91 37 114 17 26 0 1445 51 03368 320602

298 rows × 9 columns

In [92]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [93]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [94]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [95]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [96]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [97]:
df_acs2021_comb
Out[97]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract
0 37 114 17 26 0 1445 51 03368 320602
1 45 5 0 27 6 1433 51 03368 320601
2 107 43 18 28 11 1147 51 036 600100
3 65 20 36 23 1 990 51 036 600200
4 47 13 45 3 0 768 51 036 600300
... ... ... ... ... ... ... ... ... ...
293 90 94 107 202 150 3818 51 095 080402
294 21 50 13 89 43 1552 51 095 080403
295 63 0 85 0 183 1458 51 095 080404
296 30 103 46 48 118 1448 51 095 080208
297 84 13 18 44 38 1637 51 095 080308

298 rows × 9 columns

In [98]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[98]:
Int64Index([105, 110], dtype='int64')
In [99]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[99]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract
0 37 114 17 26 0 1445 51 03368 320602
1 45 5 0 27 6 1433 51 03368 320601
2 107 43 18 28 11 1147 51 036 600100
3 65 20 36 23 1 990 51 036 600200
4 47 13 45 3 0 768 51 036 600300
... ... ... ... ... ... ... ... ... ...
293 90 94 107 202 150 3818 51 095 080402
294 21 50 13 89 43 1552 51 095 080403
295 63 0 85 0 183 1458 51 095 080404
296 30 103 46 48 118 1448 51 095 080208
297 84 13 18 44 38 1637 51 095 080308

296 rows × 9 columns

In [100]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [101]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[101]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract GEOID
0 37 114 17 26 0 1445 51 03368 320602 5103368320602
1 45 5 0 27 6 1433 51 03368 320601 5103368320601
2 107 43 18 28 11 1147 51 036 600100 51036600100
3 65 20 36 23 1 990 51 036 600200 51036600200
4 47 13 45 3 0 768 51 036 600300 51036600300
... ... ... ... ... ... ... ... ... ... ...
293 90 94 107 202 150 3818 51 095 080402 51095080402
294 21 50 13 89 43 1552 51 095 080403 51095080403
295 63 0 85 0 183 1458 51 095 080404 51095080404
296 30 103 46 48 118 1448 51 095 080208 51095080208
297 84 13 18 44 38 1637 51 095 080308 51095080308

296 rows × 10 columns

In [102]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[102]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract GEOID
0 37 114 17 26 0 1445 51 03368 320602 51085320602
1 45 5 0 27 6 1433 51 03368 320601 51085320601
2 107 43 18 28 11 1147 51 036 600100 51036600100
3 65 20 36 23 1 990 51 036 600200 51036600200
4 47 13 45 3 0 768 51 036 600300 51036600300
... ... ... ... ... ... ... ... ... ... ...
293 90 94 107 202 150 3818 51 095 080402 51095080402
294 21 50 13 89 43 1552 51 095 080403 51095080403
295 63 0 85 0 183 1458 51 095 080404 51095080404
296 30 103 46 48 118 1448 51 095 080208 51095080208
297 84 13 18 44 38 1637 51 095 080308 51095080308

296 rows × 10 columns

In [103]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[103]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract GEOID Year
0 37 114 17 26 0 1445 51 03368 320602 51085320602 2022
1 45 5 0 27 6 1433 51 03368 320601 51085320601 2022
2 107 43 18 28 11 1147 51 036 600100 51036600100 2022
3 65 20 36 23 1 990 51 036 600200 51036600200 2022
4 47 13 45 3 0 768 51 036 600300 51036600300 2022
In [104]:
df_acs2021['B25106_006E'] = df_acs2021['B25106_006E'].astype(int)
df_acs2021['B25106_010E'] = df_acs2021['B25106_010E'].astype(int)
df_acs2021['B25106_014E'] = df_acs2021['B25106_014E'].astype(int)
df_acs2021['B25106_018E'] = df_acs2021['B25106_018E'].astype(int)
df_acs2021['B25106_022E'] = df_acs2021['B25106_022E'].astype(int)
df_acs2021['B25106_001E'] = df_acs2021['B25106_001E'].astype(int)
df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[104]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract GEOID Year
0 37 114 17 26 0 1445 51 03368 320602 51085320602 2022
1 45 5 0 27 6 1433 51 03368 320601 51085320601 2022
2 107 43 18 28 11 1147 51 036 600100 51036600100 2022
3 65 20 36 23 1 990 51 036 600200 51036600200 2022
4 47 13 45 3 0 768 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ... ... ... ... ...
293 90 94 107 202 150 3818 51 095 080402 51095080402 2022
294 21 50 13 89 43 1552 51 095 080403 51095080403 2022
295 63 0 85 0 183 1458 51 095 080404 51095080404 2022
296 30 103 46 48 118 1448 51 095 080208 51095080208 2022
297 84 13 18 44 38 1637 51 095 080308 51095080308 2022

296 rows × 11 columns

In [105]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [106]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [107]:
df_acs2021.columns
Out[107]:
Index(['B25106_006E', 'B25106_010E', 'B25106_014E', 'B25106_018E',
       'B25106_022E', 'B25106_001E', 'state', 'county', 'tract', 'GEOID',
       'Year', 'locality'],
      dtype='object')
In [108]:
df_acs2021
Out[108]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract GEOID Year locality
0 37 114 17 26 0 1445 51 03368 320602 51085320602 2022 Ashland
1 45 5 0 27 6 1433 51 03368 320601 51085320601 2022 Ashland
2 107 43 18 28 11 1147 51 036 600100 51036600100 2022 Charles City
3 65 20 36 23 1 990 51 036 600200 51036600200 2022 Charles City
4 47 13 45 3 0 768 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ... ... ... ... ...
293 90 94 107 202 150 3818 51 095 080402 51095080402 2022 James City
294 21 50 13 89 43 1552 51 095 080403 51095080403 2022 James City
295 63 0 85 0 183 1458 51 095 080404 51095080404 2022 James City
296 30 103 46 48 118 1448 51 095 080208 51095080208 2022 James City
297 84 13 18 44 38 1637 51 095 080308 51095080308 2022 James City

296 rows × 12 columns

In [ ]:
In [109]:
df_acs2021['Perc_BurdHousU'] = ((df_acs2021['B25106_006E']+df_acs2021['B25106_010E']+df_acs2021['B25106_014E']+df_acs2021['B25106_018E']+df_acs2021['B25106_022E'])/df_acs2021['B25106_001E'])*100
In [110]:
df_acs2021.head()
Out[110]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract GEOID Year locality Perc_BurdHousU
0 37 114 17 26 0 1445 51 03368 320602 51085320602 2022 Ashland 13.425606
1 45 5 0 27 6 1433 51 03368 320601 51085320601 2022 Ashland 5.792045
2 107 43 18 28 11 1147 51 036 600100 51036600100 2022 Charles City 18.047079
3 65 20 36 23 1 990 51 036 600200 51036600200 2022 Charles City 14.646465
4 47 13 45 3 0 768 51 036 600300 51036600300 2022 Charles City 14.062500
In [111]:
## Store vaariable data in new variable 

df_acs2021_var3 = df_acs2021
In [112]:
## Visualize on a map
In [113]:
gdf_BurdHousU = pd.merge(gdf_US_CT,df_acs2021_var3, on = 'GEOID', how = 'inner')
In [114]:
gdf_BurdHousU.head()
Out[114]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... B25106_014E B25106_018E B25106_022E B25106_001E state county tract Year locality Perc_BurdHousU
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... 59 109 135 2215 51 085 320100 2022 Hanover 18.148984
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... 58 70 45 1887 51 085 321201 2022 Hanover 16.163222
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... 0 33 9 1778 51 085 321202 2022 Hanover 6.524184
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... 48 115 104 2163 51 085 320400 2022 Hanover 15.580213
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... 50 70 0 1248 51 087 201002 2022 Henrico 19.310897

5 rows × 25 columns

In [115]:
import matplotlib.pyplot as plt
import os

# Assuming gdf_County_RVA and gdf_BurdHousU are already defined

fig, ax = plt.subplots(figsize=(20, 16)) 

# Plot locality boundaries
gdf_County_RVA.plot(color='White', edgecolor='k', linewidth=2, ax=ax)

# Plot values
gdf_BurdHousU.plot(column='Perc_BurdHousU', cmap='YlOrRd', ax=ax, legend=True)

# Add Title and Labels
ax.set_title('Percentage Severely Burdened Housing units (30% or more) by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Add text box
textstr = "Severely Burdened Housing Unit: % of household salary required for Housing cost > 30%"
props = dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')
ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=14,
        verticalalignment='top', horizontalalignment='right', bbox=props)

# Save the map
save_dir = '.'  # Change this to your desired directory
map_name = 'Perc_BurdHousU.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [116]:
df_acs2021_var3
Out[116]:
B25106_006E B25106_010E B25106_014E B25106_018E B25106_022E B25106_001E state county tract GEOID Year locality Perc_BurdHousU
0 37 114 17 26 0 1445 51 03368 320602 51085320602 2022 Ashland 13.425606
1 45 5 0 27 6 1433 51 03368 320601 51085320601 2022 Ashland 5.792045
2 107 43 18 28 11 1147 51 036 600100 51036600100 2022 Charles City 18.047079
3 65 20 36 23 1 990 51 036 600200 51036600200 2022 Charles City 14.646465
4 47 13 45 3 0 768 51 036 600300 51036600300 2022 Charles City 14.062500
... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 90 94 107 202 150 3818 51 095 080402 51095080402 2022 James City 16.841278
294 21 50 13 89 43 1552 51 095 080403 51095080403 2022 James City 13.917526
295 63 0 85 0 183 1458 51 095 080404 51095080404 2022 James City 22.702332
296 30 103 46 48 118 1448 51 095 080208 51095080208 2022 James City 23.825967
297 84 13 18 44 38 1637 51 095 080308 51095080308 2022 James City 12.034209

296 rows × 13 columns

In [ ]:
In [ ]:
In [117]:
## Var 4: % Housing Units with No Vehicle: DP04_0058E/DP04_0057E
In [118]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5/profile'
get = '?get='
variables = 'DP04_0058E,DP04_0057E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [119]:
query_url_HC_YM_acs2021
Out[119]:
'https://api.census.gov/data/2022/acs/acs5/profile?get=DP04_0058E,DP04_0057E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [120]:
response_HC_YM_acs2021
Out[120]:
<Response [200]>
In [121]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[121]:
DP04_0058E DP04_0057E state county tract
1 86 1147 51 036 600100
2 63 990 51 036 600200
3 37 768 51 036 600300
4 8 1421 51 041 100106
5 25 1087 51 041 100107
... ... ... ... ... ...
292 178 1116 51 760 070902
293 136 1519 51 760 071002
294 258 1491 51 760 071003
295 144 829 51 760 071004
296 162 2959 51 760 071100

296 rows × 5 columns

In [122]:
#Getting Ashland Data-
In [123]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [124]:
df_acs2021_a # Ashland Census Tracts
Out[124]:
DP04_0058E DP04_0057E state county tract
90 102 1433 51 03368 320601
91 18 1445 51 03368 320602
In [125]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[125]:
DP04_0058E DP04_0057E state county tract
1 86 1147 51 036 600100
2 63 990 51 036 600200
3 37 768 51 036 600300
4 8 1421 51 041 100106
5 25 1087 51 041 100107
... ... ... ... ... ...
294 258 1491 51 760 071003
295 144 829 51 760 071004
296 162 2959 51 760 071100
90 102 1433 51 03368 320601
91 18 1445 51 03368 320602

298 rows × 5 columns

In [126]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [127]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [128]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [129]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [130]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [131]:
df_acs2021_comb
Out[131]:
DP04_0058E DP04_0057E state county tract
0 18 1445 51 03368 320602
1 102 1433 51 03368 320601
2 86 1147 51 036 600100
3 63 990 51 036 600200
4 37 768 51 036 600300
... ... ... ... ... ...
293 151 3818 51 095 080402
294 0 1552 51 095 080403
295 18 1458 51 095 080404
296 23 1448 51 095 080208
297 31 1637 51 095 080308

298 rows × 5 columns

In [132]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[132]:
Int64Index([105, 110], dtype='int64')
In [133]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[133]:
DP04_0058E DP04_0057E state county tract
0 18 1445 51 03368 320602
1 102 1433 51 03368 320601
2 86 1147 51 036 600100
3 63 990 51 036 600200
4 37 768 51 036 600300
... ... ... ... ... ...
293 151 3818 51 095 080402
294 0 1552 51 095 080403
295 18 1458 51 095 080404
296 23 1448 51 095 080208
297 31 1637 51 095 080308

296 rows × 5 columns

In [134]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [135]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[135]:
DP04_0058E DP04_0057E state county tract GEOID
0 18 1445 51 03368 320602 5103368320602
1 102 1433 51 03368 320601 5103368320601
2 86 1147 51 036 600100 51036600100
3 63 990 51 036 600200 51036600200
4 37 768 51 036 600300 51036600300
... ... ... ... ... ... ...
293 151 3818 51 095 080402 51095080402
294 0 1552 51 095 080403 51095080403
295 18 1458 51 095 080404 51095080404
296 23 1448 51 095 080208 51095080208
297 31 1637 51 095 080308 51095080308

296 rows × 6 columns

In [136]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[136]:
DP04_0058E DP04_0057E state county tract GEOID
0 18 1445 51 03368 320602 51085320602
1 102 1433 51 03368 320601 51085320601
2 86 1147 51 036 600100 51036600100
3 63 990 51 036 600200 51036600200
4 37 768 51 036 600300 51036600300
... ... ... ... ... ... ...
293 151 3818 51 095 080402 51095080402
294 0 1552 51 095 080403 51095080403
295 18 1458 51 095 080404 51095080404
296 23 1448 51 095 080208 51095080208
297 31 1637 51 095 080308 51095080308

296 rows × 6 columns

In [137]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[137]:
DP04_0058E DP04_0057E state county tract GEOID Year
0 18 1445 51 03368 320602 51085320602 2022
1 102 1433 51 03368 320601 51085320601 2022
2 86 1147 51 036 600100 51036600100 2022
3 63 990 51 036 600200 51036600200 2022
4 37 768 51 036 600300 51036600300 2022
In [138]:
df_acs2021['DP04_0058E'] = df_acs2021['DP04_0058E'].astype(int)
df_acs2021['DP04_0057E'] = df_acs2021['DP04_0057E'].astype(int)

df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[138]:
DP04_0058E DP04_0057E state county tract GEOID Year
0 18 1445 51 03368 320602 51085320602 2022
1 102 1433 51 03368 320601 51085320601 2022
2 86 1147 51 036 600100 51036600100 2022
3 63 990 51 036 600200 51036600200 2022
4 37 768 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ...
293 151 3818 51 095 080402 51095080402 2022
294 0 1552 51 095 080403 51095080403 2022
295 18 1458 51 095 080404 51095080404 2022
296 23 1448 51 095 080208 51095080208 2022
297 31 1637 51 095 080308 51095080308 2022

296 rows × 7 columns

In [139]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [140]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [141]:
df_acs2021.columns
Out[141]:
Index(['DP04_0058E', 'DP04_0057E', 'state', 'county', 'tract', 'GEOID', 'Year',
       'locality'],
      dtype='object')
In [142]:
df_acs2021
Out[142]:
DP04_0058E DP04_0057E state county tract GEOID Year locality
0 18 1445 51 03368 320602 51085320602 2022 Ashland
1 102 1433 51 03368 320601 51085320601 2022 Ashland
2 86 1147 51 036 600100 51036600100 2022 Charles City
3 63 990 51 036 600200 51036600200 2022 Charles City
4 37 768 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ...
293 151 3818 51 095 080402 51095080402 2022 James City
294 0 1552 51 095 080403 51095080403 2022 James City
295 18 1458 51 095 080404 51095080404 2022 James City
296 23 1448 51 095 080208 51095080208 2022 James City
297 31 1637 51 095 080308 51095080308 2022 James City

296 rows × 8 columns

In [ ]:
In [143]:
df_acs2021['Perc_NoVeh'] = (df_acs2021['DP04_0058E']/df_acs2021['DP04_0057E'])*100
In [144]:
df_acs2021.head()
Out[144]:
DP04_0058E DP04_0057E state county tract GEOID Year locality Perc_NoVeh
0 18 1445 51 03368 320602 51085320602 2022 Ashland 1.245675
1 102 1433 51 03368 320601 51085320601 2022 Ashland 7.117934
2 86 1147 51 036 600100 51036600100 2022 Charles City 7.497820
3 63 990 51 036 600200 51036600200 2022 Charles City 6.363636
4 37 768 51 036 600300 51036600300 2022 Charles City 4.817708
In [145]:
## Store vaariable data in new variable 

df_acs2021_var4 = df_acs2021
In [146]:
## Visualize on a map
In [147]:
gdf_NoVeh = pd.merge(gdf_US_CT,df_acs2021_var4, on = 'GEOID', how = 'inner')
In [148]:
gdf_NoVeh.head()
Out[148]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... INTPTLON geometry DP04_0058E DP04_0057E state county tract Year locality Perc_NoVeh
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 17 2215 51 085 320100 2022 Hanover 0.767494
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 40 1887 51 085 321201 2022 Hanover 2.119767
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 138 1778 51 085 321202 2022 Hanover 7.761530
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... -077.5209363 POLYGON ((-77.61233 37.75986, -77.61216 37.760... 91 2163 51 085 320400 2022 Hanover 4.207120
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... -077.4043263 POLYGON ((-77.41507 37.56327, -77.41492 37.563... 81 1248 51 087 201002 2022 Henrico 6.490385

5 rows × 21 columns

In [149]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_NoVeh.plot(column='Perc_NoVeh',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Housing Units with No Vehicle by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
map_name = 'Perc_NoVeh.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [150]:
df_acs2021_var4
Out[150]:
DP04_0058E DP04_0057E state county tract GEOID Year locality Perc_NoVeh
0 18 1445 51 03368 320602 51085320602 2022 Ashland 1.245675
1 102 1433 51 03368 320601 51085320601 2022 Ashland 7.117934
2 86 1147 51 036 600100 51036600100 2022 Charles City 7.497820
3 63 990 51 036 600200 51036600200 2022 Charles City 6.363636
4 37 768 51 036 600300 51036600300 2022 Charles City 4.817708
... ... ... ... ... ... ... ... ... ...
293 151 3818 51 095 080402 51095080402 2022 James City 3.954950
294 0 1552 51 095 080403 51095080403 2022 James City 0.000000
295 18 1458 51 095 080404 51095080404 2022 James City 1.234568
296 23 1448 51 095 080208 51095080208 2022 James City 1.588398
297 31 1637 51 095 080308 51095080308 2022 James City 1.893708

296 rows × 9 columns

In [ ]:
In [151]:
## Var 5: % pop with no health insurance: 'DP03_0099E,DP05_0001E'
In [152]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5/profile'
get = '?get='
variables = 'DP03_0099E,DP05_0001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [153]:
query_url_HC_YM_acs2021
Out[153]:
'https://api.census.gov/data/2022/acs/acs5/profile?get=DP03_0099E,DP05_0001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [154]:
response_HC_YM_acs2021
Out[154]:
<Response [200]>
In [155]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[155]:
DP03_0099E DP05_0001E state county tract
1 193 2722 51 036 600100
2 153 2374 51 036 600200
3 81 1664 51 036 600300
4 335 3851 51 041 100106
5 304 2511 51 041 100107
... ... ... ... ... ...
292 149 3092 51 760 070902
293 722 4257 51 760 071002
294 26 2556 51 760 071003
295 74 1821 51 760 071004
296 412 5380 51 760 071100

296 rows × 5 columns

In [156]:
#Getting Ashland Data-
In [157]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [158]:
df_acs2021_a # Ashland Census Tracts
Out[158]:
DP03_0099E DP05_0001E state county tract
90 278 5249 51 03368 320601
91 419 2952 51 03368 320602
In [159]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[159]:
DP03_0099E DP05_0001E state county tract
1 193 2722 51 036 600100
2 153 2374 51 036 600200
3 81 1664 51 036 600300
4 335 3851 51 041 100106
5 304 2511 51 041 100107
... ... ... ... ... ...
294 26 2556 51 760 071003
295 74 1821 51 760 071004
296 412 5380 51 760 071100
90 278 5249 51 03368 320601
91 419 2952 51 03368 320602

298 rows × 5 columns

In [160]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [161]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [162]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [163]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [164]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [165]:
df_acs2021_comb
Out[165]:
DP03_0099E DP05_0001E state county tract
0 419 2952 51 03368 320602
1 278 5249 51 03368 320601
2 193 2722 51 036 600100
3 153 2374 51 036 600200
4 81 1664 51 036 600300
... ... ... ... ... ...
293 216 10555 51 095 080402
294 144 3919 51 095 080403
295 155 4168 51 095 080404
296 26 3725 51 095 080208
297 194 4411 51 095 080308

298 rows × 5 columns

In [166]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[166]:
Int64Index([105, 110], dtype='int64')
In [167]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[167]:
DP03_0099E DP05_0001E state county tract
0 419 2952 51 03368 320602
1 278 5249 51 03368 320601
2 193 2722 51 036 600100
3 153 2374 51 036 600200
4 81 1664 51 036 600300
... ... ... ... ... ...
293 216 10555 51 095 080402
294 144 3919 51 095 080403
295 155 4168 51 095 080404
296 26 3725 51 095 080208
297 194 4411 51 095 080308

296 rows × 5 columns

In [168]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [169]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[169]:
DP03_0099E DP05_0001E state county tract GEOID
0 419 2952 51 03368 320602 5103368320602
1 278 5249 51 03368 320601 5103368320601
2 193 2722 51 036 600100 51036600100
3 153 2374 51 036 600200 51036600200
4 81 1664 51 036 600300 51036600300
... ... ... ... ... ... ...
293 216 10555 51 095 080402 51095080402
294 144 3919 51 095 080403 51095080403
295 155 4168 51 095 080404 51095080404
296 26 3725 51 095 080208 51095080208
297 194 4411 51 095 080308 51095080308

296 rows × 6 columns

In [170]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[170]:
DP03_0099E DP05_0001E state county tract GEOID
0 419 2952 51 03368 320602 51085320602
1 278 5249 51 03368 320601 51085320601
2 193 2722 51 036 600100 51036600100
3 153 2374 51 036 600200 51036600200
4 81 1664 51 036 600300 51036600300
... ... ... ... ... ... ...
293 216 10555 51 095 080402 51095080402
294 144 3919 51 095 080403 51095080403
295 155 4168 51 095 080404 51095080404
296 26 3725 51 095 080208 51095080208
297 194 4411 51 095 080308 51095080308

296 rows × 6 columns

In [171]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[171]:
DP03_0099E DP05_0001E state county tract GEOID Year
0 419 2952 51 03368 320602 51085320602 2022
1 278 5249 51 03368 320601 51085320601 2022
2 193 2722 51 036 600100 51036600100 2022
3 153 2374 51 036 600200 51036600200 2022
4 81 1664 51 036 600300 51036600300 2022
In [172]:
df_acs2021['DP03_0099E'] = df_acs2021['DP03_0099E'].astype(int)
df_acs2021['DP05_0001E'] = df_acs2021['DP05_0001E'].astype(int)

df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[172]:
DP03_0099E DP05_0001E state county tract GEOID Year
0 419 2952 51 03368 320602 51085320602 2022
1 278 5249 51 03368 320601 51085320601 2022
2 193 2722 51 036 600100 51036600100 2022
3 153 2374 51 036 600200 51036600200 2022
4 81 1664 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ...
293 216 10555 51 095 080402 51095080402 2022
294 144 3919 51 095 080403 51095080403 2022
295 155 4168 51 095 080404 51095080404 2022
296 26 3725 51 095 080208 51095080208 2022
297 194 4411 51 095 080308 51095080308 2022

296 rows × 7 columns

In [173]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [174]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [175]:
df_acs2021.columns
Out[175]:
Index(['DP03_0099E', 'DP05_0001E', 'state', 'county', 'tract', 'GEOID', 'Year',
       'locality'],
      dtype='object')
In [176]:
df_acs2021
Out[176]:
DP03_0099E DP05_0001E state county tract GEOID Year locality
0 419 2952 51 03368 320602 51085320602 2022 Ashland
1 278 5249 51 03368 320601 51085320601 2022 Ashland
2 193 2722 51 036 600100 51036600100 2022 Charles City
3 153 2374 51 036 600200 51036600200 2022 Charles City
4 81 1664 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ...
293 216 10555 51 095 080402 51095080402 2022 James City
294 144 3919 51 095 080403 51095080403 2022 James City
295 155 4168 51 095 080404 51095080404 2022 James City
296 26 3725 51 095 080208 51095080208 2022 James City
297 194 4411 51 095 080308 51095080308 2022 James City

296 rows × 8 columns

In [ ]:
In [177]:
df_acs2021['Perc_NoIns'] = (df_acs2021['DP03_0099E']/df_acs2021['DP05_0001E'])*100
In [178]:
df_acs2021.head()
Out[178]:
DP03_0099E DP05_0001E state county tract GEOID Year locality Perc_NoIns
0 419 2952 51 03368 320602 51085320602 2022 Ashland 14.193767
1 278 5249 51 03368 320601 51085320601 2022 Ashland 5.296247
2 193 2722 51 036 600100 51036600100 2022 Charles City 7.090375
3 153 2374 51 036 600200 51036600200 2022 Charles City 6.444819
4 81 1664 51 036 600300 51036600300 2022 Charles City 4.867788
In [179]:
## Store vaariable data in new variable 

df_acs2021_var5 = df_acs2021
In [180]:
## Visualize on a map
In [181]:
gdf_NoIns = pd.merge(gdf_US_CT,df_acs2021_var5, on = 'GEOID', how = 'inner')
In [182]:
gdf_NoIns.head()
Out[182]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... INTPTLON geometry DP03_0099E DP05_0001E state county tract Year locality Perc_NoIns
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 263 5516 51 085 320100 2022 Hanover 4.767948
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 152 4585 51 085 321201 2022 Hanover 3.315158
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 207 4364 51 085 321202 2022 Hanover 4.743355
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... -077.5209363 POLYGON ((-77.61233 37.75986, -77.61216 37.760... 459 5455 51 085 320400 2022 Hanover 8.414299
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... -077.4043263 POLYGON ((-77.41507 37.56327, -77.41492 37.563... 228 3238 51 087 201002 2022 Henrico 7.041384

5 rows × 21 columns

In [183]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_NoIns.plot(column='Perc_NoIns',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Population with no health insurance by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
map_name = 'Perc_NoIns.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [184]:
df_acs2021_var5
Out[184]:
DP03_0099E DP05_0001E state county tract GEOID Year locality Perc_NoIns
0 419 2952 51 03368 320602 51085320602 2022 Ashland 14.193767
1 278 5249 51 03368 320601 51085320601 2022 Ashland 5.296247
2 193 2722 51 036 600100 51036600100 2022 Charles City 7.090375
3 153 2374 51 036 600200 51036600200 2022 Charles City 6.444819
4 81 1664 51 036 600300 51036600300 2022 Charles City 4.867788
... ... ... ... ... ... ... ... ... ...
293 216 10555 51 095 080402 51095080402 2022 James City 2.046423
294 144 3919 51 095 080403 51095080403 2022 James City 3.674407
295 155 4168 51 095 080404 51095080404 2022 James City 3.718810
296 26 3725 51 095 080208 51095080208 2022 James City 0.697987
297 194 4411 51 095 080308 51095080308 2022 James City 4.398096

296 rows × 9 columns

In [ ]:
In [185]:
## Var 6 : % pop with a disability: (B18101_004E + B18101_007E + B18101_010E + B18101_013E + B18101_016E + B18101_019E + B18101_023E + B18101_026E + B18101_029E + B18101_032E + B18101_035E + B18101_038E) /B01003_001E
In [186]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5'
get = '?get='
variables = 'B18101_004E,B18101_007E,B18101_010E,B18101_013E,B18101_016E,B18101_019E,B18101_023E,B18101_026E,B18101_029E,B18101_032E,B18101_035E,B18101_038E,B01003_001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [187]:
query_url_HC_YM_acs2021
Out[187]:
'https://api.census.gov/data/2022/acs/acs5?get=B18101_004E,B18101_007E,B18101_010E,B18101_013E,B18101_016E,B18101_019E,B18101_023E,B18101_026E,B18101_029E,B18101_032E,B18101_035E,B18101_038E,B01003_001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [188]:
response_HC_YM_acs2021
Out[188]:
<Response [200]>
In [189]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[189]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract
1 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100
2 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200
3 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300
4 0 12 0 54 11 77 0 0 10 71 33 42 3851 51 041 100106
5 0 64 80 114 0 0 0 22 12 27 13 26 2511 51 041 100107
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
292 0 19 18 554 0 0 0 35 0 326 49 45 3092 51 760 070902
293 0 38 39 53 24 70 0 27 0 75 53 73 4257 51 760 071002
294 0 34 18 20 29 11 0 0 16 53 137 111 2556 51 760 071003
295 0 0 9 16 21 54 0 0 54 159 59 13 1821 51 760 071004
296 0 85 150 61 28 19 0 0 75 102 101 101 5380 51 760 071100

296 rows × 16 columns

In [190]:
#Getting Ashland Data-
In [191]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [192]:
df_acs2021_a # Ashland Census Tracts
Out[192]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract
90 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601
91 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602
In [193]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[193]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract
1 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100
2 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200
3 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300
4 0 12 0 54 11 77 0 0 10 71 33 42 3851 51 041 100106
5 0 64 80 114 0 0 0 22 12 27 13 26 2511 51 041 100107
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
294 0 34 18 20 29 11 0 0 16 53 137 111 2556 51 760 071003
295 0 0 9 16 21 54 0 0 54 159 59 13 1821 51 760 071004
296 0 85 150 61 28 19 0 0 75 102 101 101 5380 51 760 071100
90 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601
91 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602

298 rows × 16 columns

In [194]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [195]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [196]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [197]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [198]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [199]:
df_acs2021_comb
Out[199]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 0 0 111 204 201 136 0 32 0 244 59 242 10555 51 095 080402
294 0 0 15 52 135 12 0 14 0 55 60 31 3919 51 095 080403
295 0 23 0 128 74 56 0 0 19 67 70 99 4168 51 095 080404
296 0 0 41 24 116 69 0 23 51 0 87 0 3725 51 095 080208
297 0 0 15 70 102 30 24 0 9 50 97 17 4411 51 095 080308

298 rows × 16 columns

In [200]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[200]:
Int64Index([105, 110], dtype='int64')
In [201]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[201]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 0 0 111 204 201 136 0 32 0 244 59 242 10555 51 095 080402
294 0 0 15 52 135 12 0 14 0 55 60 31 3919 51 095 080403
295 0 23 0 128 74 56 0 0 19 67 70 99 4168 51 095 080404
296 0 0 41 24 116 69 0 23 51 0 87 0 3725 51 095 080208
297 0 0 15 70 102 30 24 0 9 50 97 17 4411 51 095 080308

296 rows × 16 columns

In [202]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [203]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[203]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract GEOID
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602 5103368320602
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601 5103368320601
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100 51036600100
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200 51036600200
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300 51036600300
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 0 0 111 204 201 136 0 32 0 244 59 242 10555 51 095 080402 51095080402
294 0 0 15 52 135 12 0 14 0 55 60 31 3919 51 095 080403 51095080403
295 0 23 0 128 74 56 0 0 19 67 70 99 4168 51 095 080404 51095080404
296 0 0 41 24 116 69 0 23 51 0 87 0 3725 51 095 080208 51095080208
297 0 0 15 70 102 30 24 0 9 50 97 17 4411 51 095 080308 51095080308

296 rows × 17 columns

In [204]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[204]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract GEOID
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602 51085320602
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601 51085320601
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100 51036600100
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200 51036600200
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300 51036600300
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 0 0 111 204 201 136 0 32 0 244 59 242 10555 51 095 080402 51095080402
294 0 0 15 52 135 12 0 14 0 55 60 31 3919 51 095 080403 51095080403
295 0 23 0 128 74 56 0 0 19 67 70 99 4168 51 095 080404 51095080404
296 0 0 41 24 116 69 0 23 51 0 87 0 3725 51 095 080208 51095080208
297 0 0 15 70 102 30 24 0 9 50 97 17 4411 51 095 080308 51095080308

296 rows × 17 columns

In [205]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[205]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract GEOID Year
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602 51085320602 2022
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601 51085320601 2022
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100 51036600100 2022
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200 51036600200 2022
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300 51036600300 2022
In [206]:
df_acs2021['B18101_004E'] = df_acs2021['B18101_004E'].astype(int)
df_acs2021['B18101_007E'] = df_acs2021['B18101_007E'].astype(int)
df_acs2021['B18101_010E'] = df_acs2021['B18101_010E'].astype(int)
df_acs2021['B18101_013E'] = df_acs2021['B18101_013E'].astype(int)
df_acs2021['B18101_016E'] = df_acs2021['B18101_016E'].astype(int)
df_acs2021['B18101_019E'] = df_acs2021['B18101_019E'].astype(int)
df_acs2021['B18101_023E'] = df_acs2021['B18101_023E'].astype(int)
df_acs2021['B18101_026E'] = df_acs2021['B18101_026E'].astype(int)
df_acs2021['B18101_029E'] = df_acs2021['B18101_029E'].astype(int)
df_acs2021['B18101_032E'] = df_acs2021['B18101_032E'].astype(int)
df_acs2021['B18101_035E'] = df_acs2021['B18101_035E'].astype(int)
df_acs2021['B18101_038E'] = df_acs2021['B18101_038E'].astype(int)
df_acs2021['B01003_001E'] = df_acs2021['B01003_001E'].astype(int)

df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[206]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract GEOID Year
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602 51085320602 2022
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601 51085320601 2022
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100 51036600100 2022
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200 51036600200 2022
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 0 0 111 204 201 136 0 32 0 244 59 242 10555 51 095 080402 51095080402 2022
294 0 0 15 52 135 12 0 14 0 55 60 31 3919 51 095 080403 51095080403 2022
295 0 23 0 128 74 56 0 0 19 67 70 99 4168 51 095 080404 51095080404 2022
296 0 0 41 24 116 69 0 23 51 0 87 0 3725 51 095 080208 51095080208 2022
297 0 0 15 70 102 30 24 0 9 50 97 17 4411 51 095 080308 51095080308 2022

296 rows × 18 columns

In [207]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [208]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [209]:
df_acs2021.columns
Out[209]:
Index(['B18101_004E', 'B18101_007E', 'B18101_010E', 'B18101_013E',
       'B18101_016E', 'B18101_019E', 'B18101_023E', 'B18101_026E',
       'B18101_029E', 'B18101_032E', 'B18101_035E', 'B18101_038E',
       'B01003_001E', 'state', 'county', 'tract', 'GEOID', 'Year', 'locality'],
      dtype='object')
In [210]:
df_acs2021
Out[210]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract GEOID Year locality
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602 51085320602 2022 Ashland
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601 51085320601 2022 Ashland
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100 51036600100 2022 Charles City
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200 51036600200 2022 Charles City
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 0 0 111 204 201 136 0 32 0 244 59 242 10555 51 095 080402 51095080402 2022 James City
294 0 0 15 52 135 12 0 14 0 55 60 31 3919 51 095 080403 51095080403 2022 James City
295 0 23 0 128 74 56 0 0 19 67 70 99 4168 51 095 080404 51095080404 2022 James City
296 0 0 41 24 116 69 0 23 51 0 87 0 3725 51 095 080208 51095080208 2022 James City
297 0 0 15 70 102 30 24 0 9 50 97 17 4411 51 095 080308 51095080308 2022 James City

296 rows × 19 columns

In [ ]:
In [211]:
df_acs2021['Perc_NoDis'] = (df_acs2021['B18101_004E'] + df_acs2021['B18101_007E'] + df_acs2021['B18101_010E'] + df_acs2021['B18101_013E'] + df_acs2021['B18101_016E'] + df_acs2021['B18101_019E'] + df_acs2021['B18101_023E'] + df_acs2021['B18101_026E'] + df_acs2021['B18101_029E'] + df_acs2021['B18101_032E'] + df_acs2021['B18101_035E'] + df_acs2021['B18101_038E']) /df_acs2021['B01003_001E']*100
In [212]:
df_acs2021.head()
Out[212]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract GEOID Year locality Perc_NoDis
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602 51085320602 2022 Ashland 11.382114
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601 51085320601 2022 Ashland 10.382930
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100 51036600100 2022 Charles City 16.642175
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200 51036600200 2022 Charles City 19.039596
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300 51036600300 2022 Charles City 23.377404
In [213]:
## Store vaariable data in new variable 

df_acs2021_var6 = df_acs2021
In [214]:
## Visualize on a map
In [215]:
gdf_NoDis = pd.merge(gdf_US_CT,df_acs2021_var6, on = 'GEOID', how = 'inner')
In [216]:
gdf_NoDis.head()
Out[216]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... B18101_032E B18101_035E B18101_038E B01003_001E state county tract Year locality Perc_NoDis
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... 156 138 63 5516 51 085 320100 2022 Hanover 13.324873
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... 71 139 127 4585 51 085 321201 2022 Hanover 13.740458
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... 265 28 57 4364 51 085 321202 2022 Hanover 16.544455
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... 86 27 110 5455 51 085 320400 2022 Hanover 10.265811
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... 90 84 3 3238 51 087 201002 2022 Henrico 12.970970

5 rows × 32 columns

In [217]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_NoDis.plot(column='Perc_NoDis',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Population with a disability by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
map_name = 'Perc_NoDis.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [218]:
df_acs2021_var6
Out[218]:
B18101_004E B18101_007E B18101_010E B18101_013E B18101_016E B18101_019E B18101_023E B18101_026E B18101_029E B18101_032E B18101_035E B18101_038E B01003_001E state county tract GEOID Year locality Perc_NoDis
0 0 16 15 12 13 22 0 12 16 79 116 35 2952 51 03368 320602 51085320602 2022 Ashland 11.382114
1 0 0 65 48 58 78 0 4 107 19 93 73 5249 51 03368 320601 51085320601 2022 Ashland 10.382930
2 0 6 7 74 34 65 0 8 21 112 48 78 2722 51 036 600100 51036600100 2022 Charles City 16.642175
3 0 2 5 92 51 57 0 21 19 66 54 85 2374 51 036 600200 51036600200 2022 Charles City 19.039596
4 0 14 24 61 44 46 0 0 21 44 74 61 1664 51 036 600300 51036600300 2022 Charles City 23.377404
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 0 0 111 204 201 136 0 32 0 244 59 242 10555 51 095 080402 51095080402 2022 James City 11.643771
294 0 0 15 52 135 12 0 14 0 55 60 31 3919 51 095 080403 51095080403 2022 James City 9.543251
295 0 23 0 128 74 56 0 0 19 67 70 99 4168 51 095 080404 51095080404 2022 James City 12.859885
296 0 0 41 24 116 69 0 23 51 0 87 0 3725 51 095 080208 51095080208 2022 James City 11.033557
297 0 0 15 70 102 30 24 0 9 50 97 17 4411 51 095 080308 51095080308 2022 James City 9.385627

296 rows × 20 columns

In [ ]:
In [219]:
# var 7: % pop aged 65 years and over: DP05_0024E/DP05_0001E**
In [220]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5/profile'
get = '?get='
variables = 'DP05_0024E,DP05_0001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [221]:
query_url_HC_YM_acs2021
Out[221]:
'https://api.census.gov/data/2022/acs/acs5/profile?get=DP05_0024E,DP05_0001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [222]:
response_HC_YM_acs2021
Out[222]:
<Response [200]>
In [223]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[223]:
DP05_0024E DP05_0001E state county tract
1 613 2722 51 036 600100
2 645 2374 51 036 600200
3 473 1664 51 036 600300
4 604 3851 51 041 100106
5 198 2511 51 041 100107
... ... ... ... ... ...
292 470 3092 51 760 070902
293 598 4257 51 760 071002
294 537 2556 51 760 071003
295 270 1821 51 760 071004
296 892 5380 51 760 071100

296 rows × 5 columns

In [224]:
#Getting Ashland Data-
In [225]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [226]:
df_acs2021_a # Ashland Census Tracts
Out[226]:
DP05_0024E DP05_0001E state county tract
90 621 5249 51 03368 320601
91 722 2952 51 03368 320602
In [227]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[227]:
DP05_0024E DP05_0001E state county tract
1 613 2722 51 036 600100
2 645 2374 51 036 600200
3 473 1664 51 036 600300
4 604 3851 51 041 100106
5 198 2511 51 041 100107
... ... ... ... ... ...
294 537 2556 51 760 071003
295 270 1821 51 760 071004
296 892 5380 51 760 071100
90 621 5249 51 03368 320601
91 722 2952 51 03368 320602

298 rows × 5 columns

In [228]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [229]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [230]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [231]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [232]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [233]:
df_acs2021_comb
Out[233]:
DP05_0024E DP05_0001E state county tract
0 722 2952 51 03368 320602
1 621 5249 51 03368 320601
2 613 2722 51 036 600100
3 645 2374 51 036 600200
4 473 1664 51 036 600300
... ... ... ... ... ...
293 2364 10555 51 095 080402
294 618 3919 51 095 080403
295 1064 4168 51 095 080404
296 1374 3725 51 095 080208
297 1126 4411 51 095 080308

298 rows × 5 columns

In [234]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[234]:
Int64Index([105, 110], dtype='int64')
In [235]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[235]:
DP05_0024E DP05_0001E state county tract
0 722 2952 51 03368 320602
1 621 5249 51 03368 320601
2 613 2722 51 036 600100
3 645 2374 51 036 600200
4 473 1664 51 036 600300
... ... ... ... ... ...
293 2364 10555 51 095 080402
294 618 3919 51 095 080403
295 1064 4168 51 095 080404
296 1374 3725 51 095 080208
297 1126 4411 51 095 080308

296 rows × 5 columns

In [236]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [237]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[237]:
DP05_0024E DP05_0001E state county tract GEOID
0 722 2952 51 03368 320602 5103368320602
1 621 5249 51 03368 320601 5103368320601
2 613 2722 51 036 600100 51036600100
3 645 2374 51 036 600200 51036600200
4 473 1664 51 036 600300 51036600300
... ... ... ... ... ... ...
293 2364 10555 51 095 080402 51095080402
294 618 3919 51 095 080403 51095080403
295 1064 4168 51 095 080404 51095080404
296 1374 3725 51 095 080208 51095080208
297 1126 4411 51 095 080308 51095080308

296 rows × 6 columns

In [238]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[238]:
DP05_0024E DP05_0001E state county tract GEOID
0 722 2952 51 03368 320602 51085320602
1 621 5249 51 03368 320601 51085320601
2 613 2722 51 036 600100 51036600100
3 645 2374 51 036 600200 51036600200
4 473 1664 51 036 600300 51036600300
... ... ... ... ... ... ...
293 2364 10555 51 095 080402 51095080402
294 618 3919 51 095 080403 51095080403
295 1064 4168 51 095 080404 51095080404
296 1374 3725 51 095 080208 51095080208
297 1126 4411 51 095 080308 51095080308

296 rows × 6 columns

In [239]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[239]:
DP05_0024E DP05_0001E state county tract GEOID Year
0 722 2952 51 03368 320602 51085320602 2022
1 621 5249 51 03368 320601 51085320601 2022
2 613 2722 51 036 600100 51036600100 2022
3 645 2374 51 036 600200 51036600200 2022
4 473 1664 51 036 600300 51036600300 2022
In [240]:
df_acs2021['DP05_0024E'] = df_acs2021['DP05_0024E'].astype(int)
df_acs2021['DP05_0001E'] = df_acs2021['DP05_0001E'].astype(int)


df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[240]:
DP05_0024E DP05_0001E state county tract GEOID Year
0 722 2952 51 03368 320602 51085320602 2022
1 621 5249 51 03368 320601 51085320601 2022
2 613 2722 51 036 600100 51036600100 2022
3 645 2374 51 036 600200 51036600200 2022
4 473 1664 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ...
293 2364 10555 51 095 080402 51095080402 2022
294 618 3919 51 095 080403 51095080403 2022
295 1064 4168 51 095 080404 51095080404 2022
296 1374 3725 51 095 080208 51095080208 2022
297 1126 4411 51 095 080308 51095080308 2022

296 rows × 7 columns

In [241]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [242]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [243]:
df_acs2021.columns
Out[243]:
Index(['DP05_0024E', 'DP05_0001E', 'state', 'county', 'tract', 'GEOID', 'Year',
       'locality'],
      dtype='object')
In [244]:
df_acs2021
Out[244]:
DP05_0024E DP05_0001E state county tract GEOID Year locality
0 722 2952 51 03368 320602 51085320602 2022 Ashland
1 621 5249 51 03368 320601 51085320601 2022 Ashland
2 613 2722 51 036 600100 51036600100 2022 Charles City
3 645 2374 51 036 600200 51036600200 2022 Charles City
4 473 1664 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ...
293 2364 10555 51 095 080402 51095080402 2022 James City
294 618 3919 51 095 080403 51095080403 2022 James City
295 1064 4168 51 095 080404 51095080404 2022 James City
296 1374 3725 51 095 080208 51095080208 2022 James City
297 1126 4411 51 095 080308 51095080308 2022 James City

296 rows × 8 columns

In [ ]:
In [245]:
df_acs2021['Perc_Over65'] = df_acs2021['DP05_0024E'] / df_acs2021['DP05_0001E'] *100
In [246]:
df_acs2021.head()
Out[246]:
DP05_0024E DP05_0001E state county tract GEOID Year locality Perc_Over65
0 722 2952 51 03368 320602 51085320602 2022 Ashland 24.457995
1 621 5249 51 03368 320601 51085320601 2022 Ashland 11.830825
2 613 2722 51 036 600100 51036600100 2022 Charles City 22.520206
3 645 2374 51 036 600200 51036600200 2022 Charles City 27.169334
4 473 1664 51 036 600300 51036600300 2022 Charles City 28.425481
In [247]:
## Store vaariable data in new variable 

df_acs2021_var7 = df_acs2021
In [248]:
## Visualize on a map
In [249]:
gdf_Over65 = pd.merge(gdf_US_CT,df_acs2021_var7, on = 'GEOID', how = 'inner')
In [250]:
gdf_Over65.head()
Out[250]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... INTPTLON geometry DP05_0024E DP05_0001E state county tract Year locality Perc_Over65
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 1622 5516 51 085 320100 2022 Hanover 29.405366
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 1050 4585 51 085 321201 2022 Hanover 22.900763
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 393 4364 51 085 321202 2022 Hanover 9.005500
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... -077.5209363 POLYGON ((-77.61233 37.75986, -77.61216 37.760... 999 5455 51 085 320400 2022 Hanover 18.313474
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... -077.4043263 POLYGON ((-77.41507 37.56327, -77.41492 37.563... 487 3238 51 087 201002 2022 Henrico 15.040148

5 rows × 21 columns

In [251]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_Over65.plot(column='Perc_Over65',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Population over 65 years of age by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)


# Save the map
map_name = 'Perc_Over65.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [252]:
df_acs2021_var7
Out[252]:
DP05_0024E DP05_0001E state county tract GEOID Year locality Perc_Over65
0 722 2952 51 03368 320602 51085320602 2022 Ashland 24.457995
1 621 5249 51 03368 320601 51085320601 2022 Ashland 11.830825
2 613 2722 51 036 600100 51036600100 2022 Charles City 22.520206
3 645 2374 51 036 600200 51036600200 2022 Charles City 27.169334
4 473 1664 51 036 600300 51036600300 2022 Charles City 28.425481
... ... ... ... ... ... ... ... ... ...
293 2364 10555 51 095 080402 51095080402 2022 James City 22.396968
294 618 3919 51 095 080403 51095080403 2022 James City 15.769329
295 1064 4168 51 095 080404 51095080404 2022 James City 25.527831
296 1374 3725 51 095 080208 51095080208 2022 James City 36.885906
297 1126 4411 51 095 080308 51095080308 2022 James City 25.527091

296 rows × 9 columns

In [ ]:
In [253]:
# var8: % pop aged under 5 years: DP05_0005E/DP05_0001E**
In [254]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5/profile'
get = '?get='
variables = 'DP05_0005E,DP05_0001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [255]:
query_url_HC_YM_acs2021
Out[255]:
'https://api.census.gov/data/2022/acs/acs5/profile?get=DP05_0005E,DP05_0001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [256]:
response_HC_YM_acs2021
Out[256]:
<Response [200]>
In [257]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[257]:
DP05_0005E DP05_0001E state county tract
1 142 2722 51 036 600100
2 76 2374 51 036 600200
3 43 1664 51 036 600300
4 227 3851 51 041 100106
5 204 2511 51 041 100107
... ... ... ... ... ...
292 26 3092 51 760 070902
293 411 4257 51 760 071002
294 38 2556 51 760 071003
295 146 1821 51 760 071004
296 194 5380 51 760 071100

296 rows × 5 columns

In [258]:
#Getting Ashland Data-
In [259]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [260]:
df_acs2021_a # Ashland Census Tracts
Out[260]:
DP05_0005E DP05_0001E state county tract
90 221 5249 51 03368 320601
91 167 2952 51 03368 320602
In [261]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[261]:
DP05_0005E DP05_0001E state county tract
1 142 2722 51 036 600100
2 76 2374 51 036 600200
3 43 1664 51 036 600300
4 227 3851 51 041 100106
5 204 2511 51 041 100107
... ... ... ... ... ...
294 38 2556 51 760 071003
295 146 1821 51 760 071004
296 194 5380 51 760 071100
90 221 5249 51 03368 320601
91 167 2952 51 03368 320602

298 rows × 5 columns

In [262]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [263]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [264]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [265]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [266]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [267]:
df_acs2021_comb
Out[267]:
DP05_0005E DP05_0001E state county tract
0 167 2952 51 03368 320602
1 221 5249 51 03368 320601
2 142 2722 51 036 600100
3 76 2374 51 036 600200
4 43 1664 51 036 600300
... ... ... ... ... ...
293 691 10555 51 095 080402
294 246 3919 51 095 080403
295 233 4168 51 095 080404
296 82 3725 51 095 080208
297 212 4411 51 095 080308

298 rows × 5 columns

In [268]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[268]:
Int64Index([105, 110], dtype='int64')
In [269]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[269]:
DP05_0005E DP05_0001E state county tract
0 167 2952 51 03368 320602
1 221 5249 51 03368 320601
2 142 2722 51 036 600100
3 76 2374 51 036 600200
4 43 1664 51 036 600300
... ... ... ... ... ...
293 691 10555 51 095 080402
294 246 3919 51 095 080403
295 233 4168 51 095 080404
296 82 3725 51 095 080208
297 212 4411 51 095 080308

296 rows × 5 columns

In [270]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [271]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[271]:
DP05_0005E DP05_0001E state county tract GEOID
0 167 2952 51 03368 320602 5103368320602
1 221 5249 51 03368 320601 5103368320601
2 142 2722 51 036 600100 51036600100
3 76 2374 51 036 600200 51036600200
4 43 1664 51 036 600300 51036600300
... ... ... ... ... ... ...
293 691 10555 51 095 080402 51095080402
294 246 3919 51 095 080403 51095080403
295 233 4168 51 095 080404 51095080404
296 82 3725 51 095 080208 51095080208
297 212 4411 51 095 080308 51095080308

296 rows × 6 columns

In [272]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[272]:
DP05_0005E DP05_0001E state county tract GEOID
0 167 2952 51 03368 320602 51085320602
1 221 5249 51 03368 320601 51085320601
2 142 2722 51 036 600100 51036600100
3 76 2374 51 036 600200 51036600200
4 43 1664 51 036 600300 51036600300
... ... ... ... ... ... ...
293 691 10555 51 095 080402 51095080402
294 246 3919 51 095 080403 51095080403
295 233 4168 51 095 080404 51095080404
296 82 3725 51 095 080208 51095080208
297 212 4411 51 095 080308 51095080308

296 rows × 6 columns

In [273]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[273]:
DP05_0005E DP05_0001E state county tract GEOID Year
0 167 2952 51 03368 320602 51085320602 2022
1 221 5249 51 03368 320601 51085320601 2022
2 142 2722 51 036 600100 51036600100 2022
3 76 2374 51 036 600200 51036600200 2022
4 43 1664 51 036 600300 51036600300 2022
In [274]:
df_acs2021['DP05_0005E'] = df_acs2021['DP05_0005E'].astype(int)
df_acs2021['DP05_0001E'] = df_acs2021['DP05_0001E'].astype(int)


df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[274]:
DP05_0005E DP05_0001E state county tract GEOID Year
0 167 2952 51 03368 320602 51085320602 2022
1 221 5249 51 03368 320601 51085320601 2022
2 142 2722 51 036 600100 51036600100 2022
3 76 2374 51 036 600200 51036600200 2022
4 43 1664 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ...
293 691 10555 51 095 080402 51095080402 2022
294 246 3919 51 095 080403 51095080403 2022
295 233 4168 51 095 080404 51095080404 2022
296 82 3725 51 095 080208 51095080208 2022
297 212 4411 51 095 080308 51095080308 2022

296 rows × 7 columns

In [275]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [276]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [277]:
df_acs2021.columns
Out[277]:
Index(['DP05_0005E', 'DP05_0001E', 'state', 'county', 'tract', 'GEOID', 'Year',
       'locality'],
      dtype='object')
In [278]:
df_acs2021
Out[278]:
DP05_0005E DP05_0001E state county tract GEOID Year locality
0 167 2952 51 03368 320602 51085320602 2022 Ashland
1 221 5249 51 03368 320601 51085320601 2022 Ashland
2 142 2722 51 036 600100 51036600100 2022 Charles City
3 76 2374 51 036 600200 51036600200 2022 Charles City
4 43 1664 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ...
293 691 10555 51 095 080402 51095080402 2022 James City
294 246 3919 51 095 080403 51095080403 2022 James City
295 233 4168 51 095 080404 51095080404 2022 James City
296 82 3725 51 095 080208 51095080208 2022 James City
297 212 4411 51 095 080308 51095080308 2022 James City

296 rows × 8 columns

In [ ]:
In [279]:
df_acs2021['Perc_Under5'] = df_acs2021['DP05_0005E'] / df_acs2021['DP05_0001E'] *100
In [280]:
df_acs2021.head()
Out[280]:
DP05_0005E DP05_0001E state county tract GEOID Year locality Perc_Under5
0 167 2952 51 03368 320602 51085320602 2022 Ashland 5.657182
1 221 5249 51 03368 320601 51085320601 2022 Ashland 4.210326
2 142 2722 51 036 600100 51036600100 2022 Charles City 5.216752
3 76 2374 51 036 600200 51036600200 2022 Charles City 3.201348
4 43 1664 51 036 600300 51036600300 2022 Charles City 2.584135
In [281]:
## Store vaariable data in new variable 

df_acs2021_var8 = df_acs2021
In [282]:
## Visualize on a map
In [283]:
gdf_Under5 = pd.merge(gdf_US_CT,df_acs2021_var8, on = 'GEOID', how = 'inner')
In [284]:
gdf_Under5.head()
Out[284]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... INTPTLON geometry DP05_0005E DP05_0001E state county tract Year locality Perc_Under5
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 256 5516 51 085 320100 2022 Hanover 4.641044
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 134 4585 51 085 321201 2022 Hanover 2.922574
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 405 4364 51 085 321202 2022 Hanover 9.280477
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... -077.5209363 POLYGON ((-77.61233 37.75986, -77.61216 37.760... 386 5455 51 085 320400 2022 Hanover 7.076077
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... -077.4043263 POLYGON ((-77.41507 37.56327, -77.41492 37.563... 151 3238 51 087 201002 2022 Henrico 4.663372

5 rows × 21 columns

In [285]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_Under5.plot(column='Perc_Under5',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Population Under 5 years of age by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
map_name = 'Perc_Under5.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [286]:
df_acs2021_var8
Out[286]:
DP05_0005E DP05_0001E state county tract GEOID Year locality Perc_Under5
0 167 2952 51 03368 320602 51085320602 2022 Ashland 5.657182
1 221 5249 51 03368 320601 51085320601 2022 Ashland 4.210326
2 142 2722 51 036 600100 51036600100 2022 Charles City 5.216752
3 76 2374 51 036 600200 51036600200 2022 Charles City 3.201348
4 43 1664 51 036 600300 51036600300 2022 Charles City 2.584135
... ... ... ... ... ... ... ... ... ...
293 691 10555 51 095 080402 51095080402 2022 James City 6.546660
294 246 3919 51 095 080403 51095080403 2022 James City 6.277112
295 233 4168 51 095 080404 51095080404 2022 James City 5.590211
296 82 3725 51 095 080208 51095080208 2022 James City 2.201342
297 212 4411 51 095 080308 51095080308 2022 James City 4.806166

296 rows × 9 columns

In [ ]:
In [287]:
# var9: % pop that speaks english less than "very well": B06007_005E/B01003_001E*
In [288]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5'
get = '?get='
variables = 'B06007_005E,B06007_008E,B01003_001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [289]:
query_url_HC_YM_acs2021
Out[289]:
'https://api.census.gov/data/2022/acs/acs5?get=B06007_005E,B06007_008E,B01003_001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [290]:
response_HC_YM_acs2021
Out[290]:
<Response [200]>
In [291]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[291]:
B06007_005E B06007_008E B01003_001E state county tract
1 21 0 2722 51 036 600100
2 0 4 2374 51 036 600200
3 0 0 1664 51 036 600300
4 52 0 3851 51 041 100106
5 351 49 2511 51 041 100107
... ... ... ... ... ... ...
292 18 0 3092 51 760 070902
293 254 68 4257 51 760 071002
294 26 87 2556 51 760 071003
295 34 12 1821 51 760 071004
296 20 26 5380 51 760 071100

296 rows × 6 columns

In [292]:
#Getting Ashland Data-
In [293]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [294]:
df_acs2021_a # Ashland Census Tracts
Out[294]:
B06007_005E B06007_008E B01003_001E state county tract
90 116 47 5249 51 03368 320601
91 162 10 2952 51 03368 320602
In [295]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[295]:
B06007_005E B06007_008E B01003_001E state county tract
1 21 0 2722 51 036 600100
2 0 4 2374 51 036 600200
3 0 0 1664 51 036 600300
4 52 0 3851 51 041 100106
5 351 49 2511 51 041 100107
... ... ... ... ... ... ...
294 26 87 2556 51 760 071003
295 34 12 1821 51 760 071004
296 20 26 5380 51 760 071100
90 116 47 5249 51 03368 320601
91 162 10 2952 51 03368 320602

298 rows × 6 columns

In [296]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [297]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [298]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [299]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [300]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [301]:
df_acs2021_comb
Out[301]:
B06007_005E B06007_008E B01003_001E state county tract
0 162 10 2952 51 03368 320602
1 116 47 5249 51 03368 320601
2 21 0 2722 51 036 600100
3 0 4 2374 51 036 600200
4 0 0 1664 51 036 600300
... ... ... ... ... ... ...
293 76 0 10555 51 095 080402
294 0 30 3919 51 095 080403
295 35 24 4168 51 095 080404
296 0 0 3725 51 095 080208
297 59 56 4411 51 095 080308

298 rows × 6 columns

In [302]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[302]:
Int64Index([105, 110], dtype='int64')
In [303]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[303]:
B06007_005E B06007_008E B01003_001E state county tract
0 162 10 2952 51 03368 320602
1 116 47 5249 51 03368 320601
2 21 0 2722 51 036 600100
3 0 4 2374 51 036 600200
4 0 0 1664 51 036 600300
... ... ... ... ... ... ...
293 76 0 10555 51 095 080402
294 0 30 3919 51 095 080403
295 35 24 4168 51 095 080404
296 0 0 3725 51 095 080208
297 59 56 4411 51 095 080308

296 rows × 6 columns

In [304]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [305]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[305]:
B06007_005E B06007_008E B01003_001E state county tract GEOID
0 162 10 2952 51 03368 320602 5103368320602
1 116 47 5249 51 03368 320601 5103368320601
2 21 0 2722 51 036 600100 51036600100
3 0 4 2374 51 036 600200 51036600200
4 0 0 1664 51 036 600300 51036600300
... ... ... ... ... ... ... ...
293 76 0 10555 51 095 080402 51095080402
294 0 30 3919 51 095 080403 51095080403
295 35 24 4168 51 095 080404 51095080404
296 0 0 3725 51 095 080208 51095080208
297 59 56 4411 51 095 080308 51095080308

296 rows × 7 columns

In [306]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[306]:
B06007_005E B06007_008E B01003_001E state county tract GEOID
0 162 10 2952 51 03368 320602 51085320602
1 116 47 5249 51 03368 320601 51085320601
2 21 0 2722 51 036 600100 51036600100
3 0 4 2374 51 036 600200 51036600200
4 0 0 1664 51 036 600300 51036600300
... ... ... ... ... ... ... ...
293 76 0 10555 51 095 080402 51095080402
294 0 30 3919 51 095 080403 51095080403
295 35 24 4168 51 095 080404 51095080404
296 0 0 3725 51 095 080208 51095080208
297 59 56 4411 51 095 080308 51095080308

296 rows × 7 columns

In [307]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[307]:
B06007_005E B06007_008E B01003_001E state county tract GEOID Year
0 162 10 2952 51 03368 320602 51085320602 2022
1 116 47 5249 51 03368 320601 51085320601 2022
2 21 0 2722 51 036 600100 51036600100 2022
3 0 4 2374 51 036 600200 51036600200 2022
4 0 0 1664 51 036 600300 51036600300 2022
In [308]:
df_acs2021['B06007_005E'] = df_acs2021['B06007_005E'].astype(int)
df_acs2021['B01003_001E'] = df_acs2021['B01003_001E'].astype(int)
df_acs2021['B06007_008E'] = df_acs2021['B06007_008E'].astype(int)



df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[308]:
B06007_005E B06007_008E B01003_001E state county tract GEOID Year
0 162 10 2952 51 03368 320602 51085320602 2022
1 116 47 5249 51 03368 320601 51085320601 2022
2 21 0 2722 51 036 600100 51036600100 2022
3 0 4 2374 51 036 600200 51036600200 2022
4 0 0 1664 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ... ...
293 76 0 10555 51 095 080402 51095080402 2022
294 0 30 3919 51 095 080403 51095080403 2022
295 35 24 4168 51 095 080404 51095080404 2022
296 0 0 3725 51 095 080208 51095080208 2022
297 59 56 4411 51 095 080308 51095080308 2022

296 rows × 8 columns

In [309]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [310]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [311]:
df_acs2021.columns
Out[311]:
Index(['B06007_005E', 'B06007_008E', 'B01003_001E', 'state', 'county', 'tract',
       'GEOID', 'Year', 'locality'],
      dtype='object')
In [312]:
df_acs2021
Out[312]:
B06007_005E B06007_008E B01003_001E state county tract GEOID Year locality
0 162 10 2952 51 03368 320602 51085320602 2022 Ashland
1 116 47 5249 51 03368 320601 51085320601 2022 Ashland
2 21 0 2722 51 036 600100 51036600100 2022 Charles City
3 0 4 2374 51 036 600200 51036600200 2022 Charles City
4 0 0 1664 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ... ...
293 76 0 10555 51 095 080402 51095080402 2022 James City
294 0 30 3919 51 095 080403 51095080403 2022 James City
295 35 24 4168 51 095 080404 51095080404 2022 James City
296 0 0 3725 51 095 080208 51095080208 2022 James City
297 59 56 4411 51 095 080308 51095080308 2022 James City

296 rows × 9 columns

In [ ]:
In [313]:
df_acs2021['Perc_NoEng'] = (df_acs2021['B06007_005E']+df_acs2021['B06007_008E']) / df_acs2021['B01003_001E'] *100
In [314]:
df_acs2021.head()
Out[314]:
B06007_005E B06007_008E B01003_001E state county tract GEOID Year locality Perc_NoEng
0 162 10 2952 51 03368 320602 51085320602 2022 Ashland 5.826558
1 116 47 5249 51 03368 320601 51085320601 2022 Ashland 3.105353
2 21 0 2722 51 036 600100 51036600100 2022 Charles City 0.771492
3 0 4 2374 51 036 600200 51036600200 2022 Charles City 0.168492
4 0 0 1664 51 036 600300 51036600300 2022 Charles City 0.000000
In [315]:
## Store vaariable data in new variable 

df_acs2021_var9 = df_acs2021
In [316]:
## Visualize on a map
In [317]:
gdf_NoEng = pd.merge(gdf_US_CT,df_acs2021_var9, on = 'GEOID', how = 'inner')
In [318]:
gdf_NoEng.head()
Out[318]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... geometry B06007_005E B06007_008E B01003_001E state county tract Year locality Perc_NoEng
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... POLYGON ((-77.74029 37.87408, -77.73998 37.874... 0 13 5516 51 085 320100 2022 Hanover 0.235678
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... POLYGON ((-77.36115 37.60754, -77.36084 37.607... 0 0 4585 51 085 321201 2022 Hanover 0.000000
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... POLYGON ((-77.38286 37.59526, -77.38280 37.595... 10 51 4364 51 085 321202 2022 Hanover 1.397800
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... POLYGON ((-77.61233 37.75986, -77.61216 37.760... 90 0 5455 51 085 320400 2022 Hanover 1.649863
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... POLYGON ((-77.41507 37.56327, -77.41492 37.563... 0 56 3238 51 087 201002 2022 Henrico 1.729463

5 rows × 22 columns

In [319]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_NoEng.plot(column='Perc_NoEng',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Population that speaks english less than "very well" by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
map_name = 'Perc_NoEng.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [320]:
df_acs2021_var9
Out[320]:
B06007_005E B06007_008E B01003_001E state county tract GEOID Year locality Perc_NoEng
0 162 10 2952 51 03368 320602 51085320602 2022 Ashland 5.826558
1 116 47 5249 51 03368 320601 51085320601 2022 Ashland 3.105353
2 21 0 2722 51 036 600100 51036600100 2022 Charles City 0.771492
3 0 4 2374 51 036 600200 51036600200 2022 Charles City 0.168492
4 0 0 1664 51 036 600300 51036600300 2022 Charles City 0.000000
... ... ... ... ... ... ... ... ... ... ...
293 76 0 10555 51 095 080402 51095080402 2022 James City 0.720038
294 0 30 3919 51 095 080403 51095080403 2022 James City 0.765501
295 35 24 4168 51 095 080404 51095080404 2022 James City 1.415547
296 0 0 3725 51 095 080208 51095080208 2022 James City 0.000000
297 59 56 4411 51 095 080308 51095080308 2022 James City 2.607119

296 rows × 10 columns

In [ ]:
In [321]:
## Var 10: Overcrowding: (DP04_0078E + DP04_0079E)/DP04_0076E
In [322]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5/profile'
get = '?get='
variables = 'DP04_0078E,DP04_0079E,DP04_0076E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [323]:
query_url_HC_YM_acs2021
Out[323]:
'https://api.census.gov/data/2022/acs/acs5/profile?get=DP04_0078E,DP04_0079E,DP04_0076E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [324]:
response_HC_YM_acs2021
Out[324]:
<Response [200]>
In [325]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[325]:
DP04_0078E DP04_0079E DP04_0076E state county tract
1 10 0 1147 51 036 600100
2 2 0 990 51 036 600200
3 11 0 768 51 036 600300
4 0 0 1421 51 041 100106
5 24 35 1087 51 041 100107
... ... ... ... ... ... ...
292 18 18 1116 51 760 070902
293 56 47 1519 51 760 071002
294 0 0 1491 51 760 071003
295 0 0 829 51 760 071004
296 66 0 2959 51 760 071100

296 rows × 6 columns

In [326]:
#Getting Ashland Data-
In [327]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [328]:
df_acs2021_a # Ashland Census Tracts
Out[328]:
DP04_0078E DP04_0079E DP04_0076E state county tract
90 44 19 1433 51 03368 320601
91 4 0 1445 51 03368 320602
In [329]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[329]:
DP04_0078E DP04_0079E DP04_0076E state county tract
1 10 0 1147 51 036 600100
2 2 0 990 51 036 600200
3 11 0 768 51 036 600300
4 0 0 1421 51 041 100106
5 24 35 1087 51 041 100107
... ... ... ... ... ... ...
294 0 0 1491 51 760 071003
295 0 0 829 51 760 071004
296 66 0 2959 51 760 071100
90 44 19 1433 51 03368 320601
91 4 0 1445 51 03368 320602

298 rows × 6 columns

In [330]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [331]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [332]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [333]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [334]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [335]:
df_acs2021_comb
Out[335]:
DP04_0078E DP04_0079E DP04_0076E state county tract
0 4 0 1445 51 03368 320602
1 44 19 1433 51 03368 320601
2 10 0 1147 51 036 600100
3 2 0 990 51 036 600200
4 11 0 768 51 036 600300
... ... ... ... ... ... ...
293 21 0 3818 51 095 080402
294 0 0 1552 51 095 080403
295 0 0 1458 51 095 080404
296 28 0 1448 51 095 080208
297 0 0 1637 51 095 080308

298 rows × 6 columns

In [336]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[336]:
Int64Index([105, 110], dtype='int64')
In [337]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[337]:
DP04_0078E DP04_0079E DP04_0076E state county tract
0 4 0 1445 51 03368 320602
1 44 19 1433 51 03368 320601
2 10 0 1147 51 036 600100
3 2 0 990 51 036 600200
4 11 0 768 51 036 600300
... ... ... ... ... ... ...
293 21 0 3818 51 095 080402
294 0 0 1552 51 095 080403
295 0 0 1458 51 095 080404
296 28 0 1448 51 095 080208
297 0 0 1637 51 095 080308

296 rows × 6 columns

In [338]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [339]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[339]:
DP04_0078E DP04_0079E DP04_0076E state county tract GEOID
0 4 0 1445 51 03368 320602 5103368320602
1 44 19 1433 51 03368 320601 5103368320601
2 10 0 1147 51 036 600100 51036600100
3 2 0 990 51 036 600200 51036600200
4 11 0 768 51 036 600300 51036600300
... ... ... ... ... ... ... ...
293 21 0 3818 51 095 080402 51095080402
294 0 0 1552 51 095 080403 51095080403
295 0 0 1458 51 095 080404 51095080404
296 28 0 1448 51 095 080208 51095080208
297 0 0 1637 51 095 080308 51095080308

296 rows × 7 columns

In [340]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[340]:
DP04_0078E DP04_0079E DP04_0076E state county tract GEOID
0 4 0 1445 51 03368 320602 51085320602
1 44 19 1433 51 03368 320601 51085320601
2 10 0 1147 51 036 600100 51036600100
3 2 0 990 51 036 600200 51036600200
4 11 0 768 51 036 600300 51036600300
... ... ... ... ... ... ... ...
293 21 0 3818 51 095 080402 51095080402
294 0 0 1552 51 095 080403 51095080403
295 0 0 1458 51 095 080404 51095080404
296 28 0 1448 51 095 080208 51095080208
297 0 0 1637 51 095 080308 51095080308

296 rows × 7 columns

In [341]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[341]:
DP04_0078E DP04_0079E DP04_0076E state county tract GEOID Year
0 4 0 1445 51 03368 320602 51085320602 2022
1 44 19 1433 51 03368 320601 51085320601 2022
2 10 0 1147 51 036 600100 51036600100 2022
3 2 0 990 51 036 600200 51036600200 2022
4 11 0 768 51 036 600300 51036600300 2022
In [342]:
df_acs2021['DP04_0078E'] = df_acs2021['DP04_0078E'].astype(int)
df_acs2021['DP04_0079E'] = df_acs2021['DP04_0079E'].astype(int)
df_acs2021['DP04_0076E'] = df_acs2021['DP04_0076E'].astype(int)


df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[342]:
DP04_0078E DP04_0079E DP04_0076E state county tract GEOID Year
0 4 0 1445 51 03368 320602 51085320602 2022
1 44 19 1433 51 03368 320601 51085320601 2022
2 10 0 1147 51 036 600100 51036600100 2022
3 2 0 990 51 036 600200 51036600200 2022
4 11 0 768 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ... ...
293 21 0 3818 51 095 080402 51095080402 2022
294 0 0 1552 51 095 080403 51095080403 2022
295 0 0 1458 51 095 080404 51095080404 2022
296 28 0 1448 51 095 080208 51095080208 2022
297 0 0 1637 51 095 080308 51095080308 2022

296 rows × 8 columns

In [343]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [344]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [345]:
df_acs2021.columns
Out[345]:
Index(['DP04_0078E', 'DP04_0079E', 'DP04_0076E', 'state', 'county', 'tract',
       'GEOID', 'Year', 'locality'],
      dtype='object')
In [346]:
df_acs2021
Out[346]:
DP04_0078E DP04_0079E DP04_0076E state county tract GEOID Year locality
0 4 0 1445 51 03368 320602 51085320602 2022 Ashland
1 44 19 1433 51 03368 320601 51085320601 2022 Ashland
2 10 0 1147 51 036 600100 51036600100 2022 Charles City
3 2 0 990 51 036 600200 51036600200 2022 Charles City
4 11 0 768 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ... ...
293 21 0 3818 51 095 080402 51095080402 2022 James City
294 0 0 1552 51 095 080403 51095080403 2022 James City
295 0 0 1458 51 095 080404 51095080404 2022 James City
296 28 0 1448 51 095 080208 51095080208 2022 James City
297 0 0 1637 51 095 080308 51095080308 2022 James City

296 rows × 9 columns

In [ ]:
In [347]:
df_acs2021['Perc_Crowd'] = (df_acs2021['DP04_0078E'] + df_acs2021['DP04_0079E']) / df_acs2021['DP04_0076E'] *100
In [348]:
df_acs2021.head()
Out[348]:
DP04_0078E DP04_0079E DP04_0076E state county tract GEOID Year locality Perc_Crowd
0 4 0 1445 51 03368 320602 51085320602 2022 Ashland 0.276817
1 44 19 1433 51 03368 320601 51085320601 2022 Ashland 4.396371
2 10 0 1147 51 036 600100 51036600100 2022 Charles City 0.871840
3 2 0 990 51 036 600200 51036600200 2022 Charles City 0.202020
4 11 0 768 51 036 600300 51036600300 2022 Charles City 1.432292
In [349]:
## Store vaariable data in new variable 

df_acs2021_var10 = df_acs2021
In [350]:
## Visualize on a map
In [351]:
gdf_Crowd = pd.merge(gdf_US_CT,df_acs2021_var10, on = 'GEOID', how = 'inner')
In [352]:
gdf_Crowd.head()
Out[352]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... geometry DP04_0078E DP04_0079E DP04_0076E state county tract Year locality Perc_Crowd
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... POLYGON ((-77.74029 37.87408, -77.73998 37.874... 0 13 2215 51 085 320100 2022 Hanover 0.586907
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... POLYGON ((-77.36115 37.60754, -77.36084 37.607... 0 0 1887 51 085 321201 2022 Hanover 0.000000
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... POLYGON ((-77.38286 37.59526, -77.38280 37.595... 0 26 1778 51 085 321202 2022 Hanover 1.462317
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... POLYGON ((-77.61233 37.75986, -77.61216 37.760... 0 50 2163 51 085 320400 2022 Hanover 2.311604
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... POLYGON ((-77.41507 37.56327, -77.41492 37.563... 0 0 1248 51 087 201002 2022 Henrico 0.000000

5 rows × 22 columns

In [353]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_Crowd.plot(column='Perc_Crowd',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Households overcrowded by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Add text box
textstr = "Overcrowding: More than 1 person per room"
props = dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')
ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=14,
        verticalalignment='top', horizontalalignment='right', bbox=props)

# Save the map
map_name = 'Perc_Crowd.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [354]:
df_acs2021_var10
Out[354]:
DP04_0078E DP04_0079E DP04_0076E state county tract GEOID Year locality Perc_Crowd
0 4 0 1445 51 03368 320602 51085320602 2022 Ashland 0.276817
1 44 19 1433 51 03368 320601 51085320601 2022 Ashland 4.396371
2 10 0 1147 51 036 600100 51036600100 2022 Charles City 0.871840
3 2 0 990 51 036 600200 51036600200 2022 Charles City 0.202020
4 11 0 768 51 036 600300 51036600300 2022 Charles City 1.432292
... ... ... ... ... ... ... ... ... ... ...
293 21 0 3818 51 095 080402 51095080402 2022 James City 0.550026
294 0 0 1552 51 095 080403 51095080403 2022 James City 0.000000
295 0 0 1458 51 095 080404 51095080404 2022 James City 0.000000
296 28 0 1448 51 095 080208 51095080208 2022 James City 1.933702
297 0 0 1637 51 095 080308 51095080308 2022 James City 0.000000

296 rows × 10 columns

In [ ]:
In [355]:
## var11: Communities of color/Underserved Populations: (B01001H_001E + B01001B_001E + B01001C_001E + B01001D_001E + B01001E_001E + B01001I_001E)/B01003_001E*
In [356]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5'
get = '?get='
variables = 'B01001H_001E,B01001B_001E,B01001C_001E,B01001D_001E,B01001E_001E,B01001I_001E,B01003_001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [357]:
query_url_HC_YM_acs2021
Out[357]:
'https://api.census.gov/data/2022/acs/acs5?get=B01001H_001E,B01001B_001E,B01001C_001E,B01001D_001E,B01001E_001E,B01001I_001E,B01003_001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [358]:
response_HC_YM_acs2021
Out[358]:
<Response [200]>
In [359]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[359]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract
1 1470 1023 85 0 0 81 2722 51 036 600100
2 819 1074 286 1 0 65 2374 51 036 600200
3 669 871 15 12 0 0 1664 51 036 600300
4 3200 299 0 17 0 95 3851 51 041 100106
5 548 1058 5 91 0 841 2511 51 041 100107
... ... ... ... ... ... ... ... ... ... ...
292 459 2623 0 4 0 6 3092 51 760 070902
293 900 2273 15 106 0 609 4257 51 760 071002
294 477 1983 0 22 0 85 2556 51 760 071003
295 234 1388 0 12 0 133 1821 51 760 071004
296 2362 2337 15 84 0 225 5380 51 760 071100

296 rows × 10 columns

In [360]:
#Getting Ashland Data-
In [361]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [362]:
df_acs2021_a # Ashland Census Tracts
Out[362]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract
90 3433 1198 38 82 0 301 5249 51 03368 320601
91 1864 426 0 56 0 587 2952 51 03368 320602
In [363]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[363]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract
1 1470 1023 85 0 0 81 2722 51 036 600100
2 819 1074 286 1 0 65 2374 51 036 600200
3 669 871 15 12 0 0 1664 51 036 600300
4 3200 299 0 17 0 95 3851 51 041 100106
5 548 1058 5 91 0 841 2511 51 041 100107
... ... ... ... ... ... ... ... ... ... ...
294 477 1983 0 22 0 85 2556 51 760 071003
295 234 1388 0 12 0 133 1821 51 760 071004
296 2362 2337 15 84 0 225 5380 51 760 071100
90 3433 1198 38 82 0 301 5249 51 03368 320601
91 1864 426 0 56 0 587 2952 51 03368 320602

298 rows × 10 columns

In [364]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [365]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [366]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [367]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [368]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [369]:
df_acs2021_comb
Out[369]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract
0 1864 426 0 56 0 587 2952 51 03368 320602
1 3433 1198 38 82 0 301 5249 51 03368 320601
2 1470 1023 85 0 0 81 2722 51 036 600100
3 819 1074 286 1 0 65 2374 51 036 600200
4 669 871 15 12 0 0 1664 51 036 600300
... ... ... ... ... ... ... ... ... ... ...
293 7569 1830 0 54 0 572 10555 51 095 080402
294 2976 273 0 121 0 182 3919 51 095 080403
295 2925 638 0 271 0 249 4168 51 095 080404
296 3292 112 0 28 0 157 3725 51 095 080208
297 3698 225 0 158 0 151 4411 51 095 080308

298 rows × 10 columns

In [370]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[370]:
Int64Index([105, 110], dtype='int64')
In [371]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[371]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract
0 1864 426 0 56 0 587 2952 51 03368 320602
1 3433 1198 38 82 0 301 5249 51 03368 320601
2 1470 1023 85 0 0 81 2722 51 036 600100
3 819 1074 286 1 0 65 2374 51 036 600200
4 669 871 15 12 0 0 1664 51 036 600300
... ... ... ... ... ... ... ... ... ... ...
293 7569 1830 0 54 0 572 10555 51 095 080402
294 2976 273 0 121 0 182 3919 51 095 080403
295 2925 638 0 271 0 249 4168 51 095 080404
296 3292 112 0 28 0 157 3725 51 095 080208
297 3698 225 0 158 0 151 4411 51 095 080308

296 rows × 10 columns

In [372]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [373]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[373]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract GEOID
0 1864 426 0 56 0 587 2952 51 03368 320602 5103368320602
1 3433 1198 38 82 0 301 5249 51 03368 320601 5103368320601
2 1470 1023 85 0 0 81 2722 51 036 600100 51036600100
3 819 1074 286 1 0 65 2374 51 036 600200 51036600200
4 669 871 15 12 0 0 1664 51 036 600300 51036600300
... ... ... ... ... ... ... ... ... ... ... ...
293 7569 1830 0 54 0 572 10555 51 095 080402 51095080402
294 2976 273 0 121 0 182 3919 51 095 080403 51095080403
295 2925 638 0 271 0 249 4168 51 095 080404 51095080404
296 3292 112 0 28 0 157 3725 51 095 080208 51095080208
297 3698 225 0 158 0 151 4411 51 095 080308 51095080308

296 rows × 11 columns

In [374]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[374]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract GEOID
0 1864 426 0 56 0 587 2952 51 03368 320602 51085320602
1 3433 1198 38 82 0 301 5249 51 03368 320601 51085320601
2 1470 1023 85 0 0 81 2722 51 036 600100 51036600100
3 819 1074 286 1 0 65 2374 51 036 600200 51036600200
4 669 871 15 12 0 0 1664 51 036 600300 51036600300
... ... ... ... ... ... ... ... ... ... ... ...
293 7569 1830 0 54 0 572 10555 51 095 080402 51095080402
294 2976 273 0 121 0 182 3919 51 095 080403 51095080403
295 2925 638 0 271 0 249 4168 51 095 080404 51095080404
296 3292 112 0 28 0 157 3725 51 095 080208 51095080208
297 3698 225 0 158 0 151 4411 51 095 080308 51095080308

296 rows × 11 columns

In [375]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[375]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract GEOID Year
0 1864 426 0 56 0 587 2952 51 03368 320602 51085320602 2022
1 3433 1198 38 82 0 301 5249 51 03368 320601 51085320601 2022
2 1470 1023 85 0 0 81 2722 51 036 600100 51036600100 2022
3 819 1074 286 1 0 65 2374 51 036 600200 51036600200 2022
4 669 871 15 12 0 0 1664 51 036 600300 51036600300 2022
In [376]:
df_acs2021['B01001H_001E'] = df_acs2021['B01001H_001E'].astype(int)
df_acs2021['B01001B_001E'] = df_acs2021['B01001B_001E'].astype(int)
df_acs2021['B01001C_001E'] = df_acs2021['B01001C_001E'].astype(int)
df_acs2021['B01001D_001E'] = df_acs2021['B01001D_001E'].astype(int)
df_acs2021['B01001E_001E'] = df_acs2021['B01001E_001E'].astype(int)
df_acs2021['B01001I_001E'] = df_acs2021['B01001I_001E'].astype(int)
df_acs2021['B01003_001E'] = df_acs2021['B01003_001E'].astype(int)


df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[376]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract GEOID Year
0 1864 426 0 56 0 587 2952 51 03368 320602 51085320602 2022
1 3433 1198 38 82 0 301 5249 51 03368 320601 51085320601 2022
2 1470 1023 85 0 0 81 2722 51 036 600100 51036600100 2022
3 819 1074 286 1 0 65 2374 51 036 600200 51036600200 2022
4 669 871 15 12 0 0 1664 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ... ... ... ... ... ...
293 7569 1830 0 54 0 572 10555 51 095 080402 51095080402 2022
294 2976 273 0 121 0 182 3919 51 095 080403 51095080403 2022
295 2925 638 0 271 0 249 4168 51 095 080404 51095080404 2022
296 3292 112 0 28 0 157 3725 51 095 080208 51095080208 2022
297 3698 225 0 158 0 151 4411 51 095 080308 51095080308 2022

296 rows × 12 columns

In [377]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [378]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [379]:
df_acs2021.columns
Out[379]:
Index(['B01001H_001E', 'B01001B_001E', 'B01001C_001E', 'B01001D_001E',
       'B01001E_001E', 'B01001I_001E', 'B01003_001E', 'state', 'county',
       'tract', 'GEOID', 'Year', 'locality'],
      dtype='object')
In [380]:
df_acs2021
Out[380]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract GEOID Year locality
0 1864 426 0 56 0 587 2952 51 03368 320602 51085320602 2022 Ashland
1 3433 1198 38 82 0 301 5249 51 03368 320601 51085320601 2022 Ashland
2 1470 1023 85 0 0 81 2722 51 036 600100 51036600100 2022 Charles City
3 819 1074 286 1 0 65 2374 51 036 600200 51036600200 2022 Charles City
4 669 871 15 12 0 0 1664 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 7569 1830 0 54 0 572 10555 51 095 080402 51095080402 2022 James City
294 2976 273 0 121 0 182 3919 51 095 080403 51095080403 2022 James City
295 2925 638 0 271 0 249 4168 51 095 080404 51095080404 2022 James City
296 3292 112 0 28 0 157 3725 51 095 080208 51095080208 2022 James City
297 3698 225 0 158 0 151 4411 51 095 080308 51095080308 2022 James City

296 rows × 13 columns

In [ ]:
In [381]:
df_acs2021['Perc_UnS'] = (df_acs2021['B01001B_001E'] + df_acs2021['B01001C_001E'] + df_acs2021['B01001D_001E'] + df_acs2021['B01001E_001E'] + df_acs2021['B01001I_001E']) /df_acs2021['B01003_001E']*100
In [382]:
df_acs2021.head()
Out[382]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract GEOID Year locality Perc_UnS
0 1864 426 0 56 0 587 2952 51 03368 320602 51085320602 2022 Ashland 36.212737
1 3433 1198 38 82 0 301 5249 51 03368 320601 51085320601 2022 Ashland 30.843970
2 1470 1023 85 0 0 81 2722 51 036 600100 51036600100 2022 Charles City 43.681117
3 819 1074 286 1 0 65 2374 51 036 600200 51036600200 2022 Charles City 60.067397
4 669 871 15 12 0 0 1664 51 036 600300 51036600300 2022 Charles City 53.966346
In [383]:
df_acs2021.sort_values(by='Perc_UnS', ascending=False)
Out[383]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract GEOID Year locality Perc_UnS
141 10 2360 0 0 0 17 2393 51 087 201405 51087201405 2022 Henrico 99.331383
265 24 3762 0 0 0 21 3814 51 760 020200 51760020200 2022 Richmond 99.187205
143 78 2326 0 28 0 38 2470 51 087 201206 51087201206 2022 Henrico 96.842105
234 125 2280 0 38 0 3408 5916 51 760 070601 51760070601 2022 Richmond 96.788371
149 209 6049 0 8 0 307 6674 51 087 201003 51087201003 2022 Henrico 95.355109
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
207 2447 48 0 19 0 37 2614 51 760 050600 51760050600 2022 Richmond 3.978577
204 5139 141 0 0 0 53 5514 51 145 500102 51145500102 2022 Powhatan 3.518317
183 3803 0 24 67 0 41 4046 51 087 200202 51087200202 2022 Henrico 3.262481
87 3163 55 0 0 0 30 3434 51 085 321402 51085321402 2022 Hanover 2.475248
132 0 0 0 0 0 0 0 51 087 980100 51087980100 2022 Henrico NaN

296 rows × 14 columns

In [384]:
## Store vaariable data in new variable 

df_acs2021_var11 = df_acs2021
In [385]:
## Visualize on a map
In [386]:
gdf_UnS = pd.merge(gdf_US_CT,df_acs2021_var11, on = 'GEOID', how = 'inner')
In [387]:
gdf_UnS.head()
Out[387]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract Year locality Perc_UnS
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... 18 0 406 5516 51 085 320100 2022 Hanover 13.306744
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... 0 0 239 4585 51 085 321201 2022 Hanover 15.637950
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... 13 19 213 4364 51 085 321202 2022 Hanover 16.544455
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... 73 0 452 5455 51 085 320400 2022 Hanover 19.046746
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... 73 0 24 3238 51 087 201002 2022 Henrico 79.956763

5 rows × 26 columns

In [388]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_UnS.plot(column='Perc_UnS',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Population of color by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
# Add text box
textstr = "Population of color: All races except white"
props = dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')
ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=14,
        verticalalignment='top', horizontalalignment='right', bbox=props)

# Save the map
map_name = 'Perc_UnS.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [389]:
df_acs2021_var11
Out[389]:
B01001H_001E B01001B_001E B01001C_001E B01001D_001E B01001E_001E B01001I_001E B01003_001E state county tract GEOID Year locality Perc_UnS
0 1864 426 0 56 0 587 2952 51 03368 320602 51085320602 2022 Ashland 36.212737
1 3433 1198 38 82 0 301 5249 51 03368 320601 51085320601 2022 Ashland 30.843970
2 1470 1023 85 0 0 81 2722 51 036 600100 51036600100 2022 Charles City 43.681117
3 819 1074 286 1 0 65 2374 51 036 600200 51036600200 2022 Charles City 60.067397
4 669 871 15 12 0 0 1664 51 036 600300 51036600300 2022 Charles City 53.966346
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
293 7569 1830 0 54 0 572 10555 51 095 080402 51095080402 2022 James City 23.268593
294 2976 273 0 121 0 182 3919 51 095 080403 51095080403 2022 James City 14.697627
295 2925 638 0 271 0 249 4168 51 095 080404 51095080404 2022 James City 27.783109
296 3292 112 0 28 0 157 3725 51 095 080208 51095080208 2022 James City 7.973154
297 3698 225 0 158 0 151 4411 51 095 080308 51095080308 2022 James City 12.106098

296 rows × 14 columns

In [ ]:
In [390]:
## Var 12: Pregnant Women: Using Proxy variable - % Women who had a birth in the past 12 months: B13002_002E/B01003_001E
In [391]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5'
get = '?get='
variables = 'B13002_002E,B01003_001E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [392]:
query_url_HC_YM_acs2021
Out[392]:
'https://api.census.gov/data/2022/acs/acs5?get=B13002_002E,B01003_001E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [393]:
response_HC_YM_acs2021
Out[393]:
<Response [200]>
In [394]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021
Out[394]:
B13002_002E B01003_001E state county tract
1 21 2722 51 036 600100
2 18 2374 51 036 600200
3 0 1664 51 036 600300
4 15 3851 51 041 100106
5 50 2511 51 041 100107
... ... ... ... ... ...
292 0 3092 51 760 070902
293 15 4257 51 760 071002
294 94 2556 51 760 071003
295 0 1821 51 760 071004
296 0 5380 51 760 071100

296 rows × 5 columns

In [395]:
#Getting Ashland Data-
In [396]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [397]:
df_acs2021_a # Ashland Census Tracts
Out[397]:
B13002_002E B01003_001E state county tract
90 73 5249 51 03368 320601
91 42 2952 51 03368 320602
In [398]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb
Out[398]:
B13002_002E B01003_001E state county tract
1 21 2722 51 036 600100
2 18 2374 51 036 600200
3 0 1664 51 036 600300
4 15 3851 51 041 100106
5 50 2511 51 041 100107
... ... ... ... ... ...
294 94 2556 51 760 071003
295 0 1821 51 760 071004
296 0 5380 51 760 071100
90 73 5249 51 03368 320601
91 42 2952 51 03368 320602

298 rows × 5 columns

In [399]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [400]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [401]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [402]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [403]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [404]:
df_acs2021_comb
Out[404]:
B13002_002E B01003_001E state county tract
0 42 2952 51 03368 320602
1 73 5249 51 03368 320601
2 21 2722 51 036 600100
3 18 2374 51 036 600200
4 0 1664 51 036 600300
... ... ... ... ... ...
293 92 10555 51 095 080402
294 75 3919 51 095 080403
295 31 4168 51 095 080404
296 0 3725 51 095 080208
297 22 4411 51 095 080308

298 rows × 5 columns

In [405]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[405]:
Int64Index([105, 110], dtype='int64')
In [406]:
df_acs2021_comb.drop(index_names, inplace = True)
df_acs2021_comb
Out[406]:
B13002_002E B01003_001E state county tract
0 42 2952 51 03368 320602
1 73 5249 51 03368 320601
2 21 2722 51 036 600100
3 18 2374 51 036 600200
4 0 1664 51 036 600300
... ... ... ... ... ...
293 92 10555 51 095 080402
294 75 3919 51 095 080403
295 31 4168 51 095 080404
296 0 3725 51 095 080208
297 22 4411 51 095 080308

296 rows × 5 columns

In [407]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [408]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
df_acs2021
Out[408]:
B13002_002E B01003_001E state county tract GEOID
0 42 2952 51 03368 320602 5103368320602
1 73 5249 51 03368 320601 5103368320601
2 21 2722 51 036 600100 51036600100
3 18 2374 51 036 600200 51036600200
4 0 1664 51 036 600300 51036600300
... ... ... ... ... ... ...
293 92 10555 51 095 080402 51095080402
294 75 3919 51 095 080403 51095080403
295 31 4168 51 095 080404 51095080404
296 0 3725 51 095 080208 51095080208
297 22 4411 51 095 080308 51095080308

296 rows × 6 columns

In [409]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
df_acs2021
Out[409]:
B13002_002E B01003_001E state county tract GEOID
0 42 2952 51 03368 320602 51085320602
1 73 5249 51 03368 320601 51085320601
2 21 2722 51 036 600100 51036600100
3 18 2374 51 036 600200 51036600200
4 0 1664 51 036 600300 51036600300
... ... ... ... ... ... ...
293 92 10555 51 095 080402 51095080402
294 75 3919 51 095 080403 51095080403
295 31 4168 51 095 080404 51095080404
296 0 3725 51 095 080208 51095080208
297 22 4411 51 095 080308 51095080308

296 rows × 6 columns

In [410]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[410]:
B13002_002E B01003_001E state county tract GEOID Year
0 42 2952 51 03368 320602 51085320602 2022
1 73 5249 51 03368 320601 51085320601 2022
2 21 2722 51 036 600100 51036600100 2022
3 18 2374 51 036 600200 51036600200 2022
4 0 1664 51 036 600300 51036600300 2022
In [411]:
df_acs2021['B13002_002E'] = df_acs2021['B13002_002E'].astype(int)
df_acs2021['B01003_001E'] = df_acs2021['B01003_001E'].astype(int)


df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021
Out[411]:
B13002_002E B01003_001E state county tract GEOID Year
0 42 2952 51 03368 320602 51085320602 2022
1 73 5249 51 03368 320601 51085320601 2022
2 21 2722 51 036 600100 51036600100 2022
3 18 2374 51 036 600200 51036600200 2022
4 0 1664 51 036 600300 51036600300 2022
... ... ... ... ... ... ... ...
293 92 10555 51 095 080402 51095080402 2022
294 75 3919 51 095 080403 51095080403 2022
295 31 4168 51 095 080404 51095080404 2022
296 0 3725 51 095 080208 51095080208 2022
297 22 4411 51 095 080308 51095080308 2022

296 rows × 7 columns

In [412]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [413]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [414]:
df_acs2021.columns
Out[414]:
Index(['B13002_002E', 'B01003_001E', 'state', 'county', 'tract', 'GEOID',
       'Year', 'locality'],
      dtype='object')
In [415]:
df_acs2021
Out[415]:
B13002_002E B01003_001E state county tract GEOID Year locality
0 42 2952 51 03368 320602 51085320602 2022 Ashland
1 73 5249 51 03368 320601 51085320601 2022 Ashland
2 21 2722 51 036 600100 51036600100 2022 Charles City
3 18 2374 51 036 600200 51036600200 2022 Charles City
4 0 1664 51 036 600300 51036600300 2022 Charles City
... ... ... ... ... ... ... ... ...
293 92 10555 51 095 080402 51095080402 2022 James City
294 75 3919 51 095 080403 51095080403 2022 James City
295 31 4168 51 095 080404 51095080404 2022 James City
296 0 3725 51 095 080208 51095080208 2022 James City
297 22 4411 51 095 080308 51095080308 2022 James City

296 rows × 8 columns

In [ ]:
In [416]:
df_acs2021['Perc_Preg12'] = df_acs2021['B13002_002E'] / df_acs2021['B01003_001E'] *100
In [417]:
df_acs2021.head()
Out[417]:
B13002_002E B01003_001E state county tract GEOID Year locality Perc_Preg12
0 42 2952 51 03368 320602 51085320602 2022 Ashland 1.422764
1 73 5249 51 03368 320601 51085320601 2022 Ashland 1.390741
2 21 2722 51 036 600100 51036600100 2022 Charles City 0.771492
3 18 2374 51 036 600200 51036600200 2022 Charles City 0.758214
4 0 1664 51 036 600300 51036600300 2022 Charles City 0.000000
In [418]:
## Store vaariable data in new variable 

df_acs2021_var12 = df_acs2021
In [419]:
## Visualize on a map
In [420]:
gdf_Preg12 = pd.merge(gdf_US_CT,df_acs2021_var12, on = 'GEOID', how = 'inner')
In [421]:
gdf_Preg12.head()
Out[421]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... INTPTLON geometry B13002_002E B01003_001E state county tract Year locality Perc_Preg12
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 26 5516 51 085 320100 2022 Hanover 0.471356
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 7 4585 51 085 321201 2022 Hanover 0.152672
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 26 4364 51 085 321202 2022 Hanover 0.595784
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 ... -077.5209363 POLYGON ((-77.61233 37.75986, -77.61216 37.760... 101 5455 51 085 320400 2022 Hanover 1.851512
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 ... -077.4043263 POLYGON ((-77.41507 37.56327, -77.41492 37.563... 9 3238 51 087 201002 2022 Henrico 0.277949

5 rows × 21 columns

In [422]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_Preg12.plot(column='Perc_Preg12',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Percentage Women who had a birth in the past 12 months', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
map_name = 'Perc_Preg12.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [423]:
df_acs2021_var12
Out[423]:
B13002_002E B01003_001E state county tract GEOID Year locality Perc_Preg12
0 42 2952 51 03368 320602 51085320602 2022 Ashland 1.422764
1 73 5249 51 03368 320601 51085320601 2022 Ashland 1.390741
2 21 2722 51 036 600100 51036600100 2022 Charles City 0.771492
3 18 2374 51 036 600200 51036600200 2022 Charles City 0.758214
4 0 1664 51 036 600300 51036600300 2022 Charles City 0.000000
... ... ... ... ... ... ... ... ... ...
293 92 10555 51 095 080402 51095080402 2022 James City 0.871625
294 75 3919 51 095 080403 51095080403 2022 James City 1.913754
295 31 4168 51 095 080404 51095080404 2022 James City 0.743762
296 0 3725 51 095 080208 51095080208 2022 James City 0.000000
297 22 4411 51 095 080308 51095080308 2022 James City 0.498753

296 rows × 9 columns

In [424]:
len(df_acs2021_var12[df_acs2021_var12['county']==760].sort_values(by='GEOID', ascending=True))
Out[424]:
0
In [425]:
len(df_acs2021_var12[df_acs2021_var12['county']=='760'])
Out[425]:
75
In [426]:
df_acs2021_var12['county'].unique()
Out[426]:
array(['03368', '036', '041', '075', '085', '087', '127', '145', '760',
       '095'], dtype=object)
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [427]:
## PIT Data
In [428]:
df_HL = gpd.read_file(r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\PIT Data\PITSurvey_Jan_June_CT.shp")
In [429]:
df_HL
Out[429]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON Point_Coun Shape_Leng Shape_Area Point_Co_1 HL_Pop geometry
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 0 1.198028 0.033940 0 0.0 MULTIPOLYGON (((-77.74024 37.87410, -77.74029 ...
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 0 0.211257 0.001150 0 0.0 POLYGON ((-77.29123 37.56062, -77.29126 37.560...
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 0 0.209468 0.000916 0 0.0 POLYGON ((-77.29616 37.56033, -77.29617 37.560...
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255.0 348336.0 +37.7231726 -077.5209363 0 0.567573 0.005988 0 0.0 POLYGON ((-77.48214 37.71538, -77.48119 37.715...
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230.0 4272.0 +37.5725532 -077.4043263 0 0.101238 0.000309 0 0.0 POLYGON ((-77.38227 37.57182, -77.38246 37.571...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
285 51 041 100806 51041100806 1008.06 Census Tract 1008.06 G5020 S 2140462.0 44827.0 +37.4336160 -077.4639027 0 0.065935 0.000222 0 0.0 POLYGON ((-77.45335 37.43758, -77.45347 37.437...
286 51 041 100922 51041100922 1009.22 Census Tract 1009.22 G5020 S 6557349.0 0.0 +37.4965151 -077.5852426 1 0.115446 0.000668 2 3.0 POLYGON ((-77.57982 37.50537, -77.57946 37.505...
287 51 041 100923 51041100923 1009.23 Census Tract 1009.23 G5020 S 3219520.0 0.0 +37.4862643 -077.5590631 0 0.077050 0.000328 0 0.0 POLYGON ((-77.56347 37.47359, -77.56359 37.473...
288 51 041 100931 51041100931 1009.31 Census Tract 1009.31 G5020 S 8610865.0 0.0 +37.4880440 -077.6140982 0 0.132519 0.000877 1 1.0 POLYGON ((-77.59651 37.49036, -77.59647 37.490...
289 51 041 100932 51041100932 1009.32 Census Tract 1009.32 G5020 S 2828103.0 0.0 +37.4686239 -077.6112349 0 0.082200 0.000288 0 0.0 POLYGON ((-77.59649 37.47376, -77.59644 37.473...

290 rows × 18 columns

In [430]:
min_val = df_HL['HL_Pop'].min()
In [431]:
max_val = df_HL['HL_Pop'].max()
In [432]:
def categorize_homeless_population(value, min_val, max_val):
    if value == min_val:
        return 1
    elif value == max_val:
        return 5
    else:
        return round((value - min_val) / (max_val - min_val) * 3) + 2
In [433]:
df_HL['HL_rating'] = df_HL['HL_Pop'].apply(lambda x: categorize_homeless_population(x, min_val, max_val))
In [434]:
df_HL.columns
Out[434]:
Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'Point_Coun',
       'Shape_Leng', 'Shape_Area', 'Point_Co_1', 'HL_Pop', 'geometry',
       'HL_rating'],
      dtype='object')
In [435]:
df_HL.head()
Out[435]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON Point_Coun Shape_Leng Shape_Area Point_Co_1 HL_Pop geometry HL_rating
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 0 1.198028 0.033940 0 0.0 MULTIPOLYGON (((-77.74024 37.87410, -77.74029 ... 1
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 0 0.211257 0.001150 0 0.0 POLYGON ((-77.29123 37.56062, -77.29126 37.560... 1
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 0 0.209468 0.000916 0 0.0 POLYGON ((-77.29616 37.56033, -77.29617 37.560... 1
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255.0 348336.0 +37.7231726 -077.5209363 0 0.567573 0.005988 0 0.0 POLYGON ((-77.48214 37.71538, -77.48119 37.715... 1
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230.0 4272.0 +37.5725532 -077.4043263 0 0.101238 0.000309 0 0.0 POLYGON ((-77.38227 37.57182, -77.38246 37.571... 1
In [436]:
df_HL.iloc[:,[3,-1]]
Out[436]:
GEOID HL_rating
0 51085320100 1
1 51085321201 1
2 51085321202 1
3 51085320400 1
4 51087201002 1
... ... ...
285 51041100806 1
286 51041100922 2
287 51041100923 1
288 51041100931 2
289 51041100932 1

290 rows × 2 columns

In [437]:
gdf_HL = pd.merge(gdf_US_CT,df_HL.iloc[:,[3,-1]], on = 'GEOID', how = 'inner')
In [438]:
gdf_HL.head()
Out[438]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry HL_rating
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 1
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 1
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 1
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 +37.7231726 -077.5209363 POLYGON ((-77.61233 37.75986, -77.61216 37.760... 1
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 +37.5725532 -077.4043263 POLYGON ((-77.41507 37.56327, -77.41492 37.563... 1
In [ ]:
In [439]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_County_RVA.plot(color='White',edgecolor='k',linewidth = 2,ax=ax) ## Locality boundaries
gdf_HL.plot(column='HL_rating',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Add Title and Labels
ax.set_title('Homeless Population Rating', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
# Add text box
textstr = "Homeless Rating: 1 to 5 for increasing homelessness"
props = dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')
ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=14,
        verticalalignment='top', horizontalalignment='right', bbox=props)

# Save the map
map_name = 'HL_rating.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [440]:
df_acs2021_var13 = df_HL.iloc[:,[3,-1]]
In [441]:
df_acs2021_var13.head()
Out[441]:
GEOID HL_rating
0 51085320100 1
1 51085321201 1
2 51085321202 1
3 51085320400 1
4 51087201002 1
In [ ]:
In [ ]:
In [ ]:
In [ ]:

BRFSS Data - health data¶

In [442]:
# Link: https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-Census-Tract-D/nw2y-v4gm/about_data

PLACES: Local Data for Better Health, Census Tract Data 2022 release¶

500 Cities & Places¶

This dataset contains model-based census tract-level estimates for the PLACES 2022 release. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 29 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations.

Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

In [443]:
df_brfss = pd.read_csv(r"C:\Users\rsingh\Downloads\PLACES__Local_Data_for_Better_Health__Census_Tract_Data_2022_release_20240417.csv")
In [444]:
df_brfss.columns
Out[444]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [445]:
df_brfss.head()
Out[445]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Data_Value_Footnote Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text
0 2020 AL Alabama Barbour 1005 1005950800 BRFSS Health Outcomes Arthritis among adults aged >=18 years % ... NaN 34.3 37.1 2055 POINT (-85.16932432 31.91226373) 1005950800 HLTHOUT ARTHRITIS CrdPrv Arthritis
1 2020 AL Alabama Bibb 1007 1007010002 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... NaN 3.2 3.6 6025 POINT (-87.0958027 33.1774109) 1007010002 HLTHOUT STROKE CrdPrv Stroke
2 2020 AL Alabama Calhoun 1015 1015000900 BRFSS Health Status Mental health not good for >=14 days among adu... % ... NaN 12.5 14.7 3362 POINT (-85.7931708 33.64793024) 1015000900 HLTHSTAT MHLTH CrdPrv Mental Health
3 2020 AL Alabama Calhoun 1015 1015002400 BRFSS Prevention Mammography use among women aged 50-74 years % ... NaN 72.0 79.0 4518 POINT (-85.77310093 33.8739283) 1015002400 PREVENT MAMMOUSE CrdPrv Mammography
4 2020 AL Alabama Cherokee 1019 1019955900 BRFSS Health Status Mental health not good for >=14 days among adu... % ... NaN 16.9 18.9 4235 POINT (-85.66863815 34.05057636) 1019955900 HLTHSTAT MHLTH CrdPrv Mental Health

5 rows × 23 columns

In [446]:
len(df_brfss)
Out[446]:
2161543
In [447]:
df=df_brfss[df_brfss['StateDesc']=='Virginia']
In [448]:
len(df)
Out[448]:
56254
In [449]:
df.head()
Out[449]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Data_Value_Footnote Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text
1442201 2020 VA Virginia Newport News 51700 51700032223 BRFSS Health Outcomes Chronic kidney disease among adults aged >=18 ... % ... NaN 1.9 2.1 3144 POINT (-76.56446023 37.16568761) 51700032223 HLTHOUT KIDNEY CrdPrv Chronic Kidney Disease
1442202 2019 VA Virginia Smyth 51173 51173030302 BRFSS Health Outcomes High cholesterol among adults aged >=18 years ... % ... NaN 35.5 37.5 3913 POINT (-81.53618819 36.85559488) 51173030302 HLTHOUT HIGHCHOL CrdPrv High Cholesterol
1442203 2020 VA Virginia Chesapeake 51550 51550020905 BRFSS Health Outcomes Depression among adults aged >=18 years % ... NaN 19.0 21.2 2701 POINT (-76.26496612 36.77303031) 51550020905 HLTHOUT DEPRESSION CrdPrv Depression
1556826 2020 VA Virginia Richmond City 51760 51760011100 BRFSS Health Risk Behaviors No leisure-time physical activity among adults... % ... NaN 21.0 27.3 2932 POINT (-77.44423332 37.56217449) 51760011100 RISKBEH LPA CrdPrv Physical Inactivity
1556827 2019 VA Virginia Virginia Beach 51810 51810046225 BRFSS Health Outcomes High cholesterol among adults aged >=18 years ... % ... NaN 29.8 30.7 4780 POINT (-76.13617028 36.78282942) 51810046225 HLTHOUT HIGHCHOL CrdPrv High Cholesterol

5 rows × 23 columns

In [450]:
df['Measure'].unique()
Out[450]:
array(['Chronic kidney disease among adults aged >=18 years',
       'High cholesterol among adults aged >=18 years who have been screened in the past 5 years',
       'Depression among adults aged >=18 years',
       'No leisure-time physical activity among adults aged >=18 years',
       'All teeth lost among adults aged >=65 years',
       'Current asthma among adults aged >=18 years',
       'Mammography use among women aged 50-74 years',
       'Fair or poor self-rated health status among adults aged >=18 years',
       'Current smoking among adults aged >=18 years',
       'Chronic obstructive pulmonary disease among adults aged >=18 years',
       'Sleeping less than 7 hours among adults aged >=18 years',
       'Diagnosed diabetes among adults aged >=18 years',
       'Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure',
       'Cervical cancer screening among adult women aged 21-65 years',
       'Current lack of health insurance among adults aged 18-64 years',
       'Cancer (excluding skin cancer) among adults aged >=18 years',
       'Cholesterol screening among adults aged >=18 years',
       'Obesity among adults aged >=18 years',
       'Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years',
       'Visits to dentist or dental clinic among adults aged >=18 years',
       'Visits to doctor for routine checkup within the past year among adults aged >=18 years',
       'Coronary heart disease among adults aged >=18 years',
       'Mental health not good for >=14 days among adults aged >=18 years',
       'High blood pressure among adults aged >=18 years',
       'Physical health not good for >=14 days among adults aged >=18 years',
       'Binge drinking among adults aged >=18 years',
       'Stroke among adults aged >=18 years',
       'Arthritis among adults aged >=18 years',
       'Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years',
       'Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening'],
      dtype=object)

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [451]:
df['Year'].unique()
Out[451]:
array([2020, 2019], dtype=int64)
In [452]:
df_asthma=df[df['Measure']=='Current asthma among adults aged >=18 years']
In [453]:
df_asthma.shape
Out[453]:
(1876, 23)
In [454]:
df_asthma.shape
Out[454]:
(1876, 23)
In [455]:
df_asthma['Year'].unique()
Out[455]:
array([2020], dtype=int64)
In [456]:
df_asthma['CountyFIPS'].unique()
Out[456]:
array([51740, 51059, 51005, 51013, 51041, 51003, 51027, 51023, 51031,
       51051, 51015, 51033, 51019, 51049, 51001, 51009, 51047, 51036,
       51007, 51037, 51035, 51053, 51025, 51045, 51057, 51011, 51017,
       51021, 51029, 51043, 51061, 51119, 51087, 51153, 51065, 51127,
       51107, 51139, 51143, 51155, 51073, 51121, 51083, 51093, 51149,
       51101, 51089, 51091, 51115, 51071, 51067, 51085, 51095, 51117,
       51069, 51075, 51099, 51105, 51131, 51109, 51125, 51063, 51145,
       51103, 51133, 51079, 51077, 51141, 51081, 51097, 51135, 51111,
       51147, 51137, 51113, 51670, 51550, 51195, 51181, 51171, 51191,
       51165, 51590, 51197, 51177, 51510, 51179, 51680, 51169, 51167,
       51700, 51710, 51199, 51540, 51161, 51650, 51175, 51630, 51690,
       51163, 51683, 51595, 51185, 51640, 51173, 51678, 51660, 51187,
       51610, 51193, 51520, 51600, 51183, 51570, 51159, 51580, 51157,
       51685, 51620, 51530, 51810, 51760, 51800, 51770, 51790, 51730,
       51775, 51840, 51735, 51820, 51750, 51830, 51720], dtype=int64)
In [457]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[457]:
66
In [458]:
df_asthma.iloc[1,:]
Out[458]:
Year                             2020
StateAbbr                          VA
StateDesc                    Virginia
CountyName                    Fairfax
CountyFIPS                      51059
                            ...      
LocationID                51059432402
CategoryID                    HLTHOUT
MeasureId                     CASTHMA
DataValueTypeID                CrdPrv
Short_Question_Text    Current Asthma
Name: 2000046, Length: 23, dtype: object
In [459]:
df['Short_Question_Text'].unique()
Out[459]:
array(['Chronic Kidney Disease', 'High Cholesterol', 'Depression',
       'Physical Inactivity', 'All Teeth Lost', 'Current Asthma',
       'Mammography', 'General Health', 'Current Smoking', 'COPD',
       'Sleep <7 hours', 'Diabetes', 'Taking BP Medication',
       'Cervical Cancer Screening', 'Health Insurance',
       'Cancer (except skin)', 'Cholesterol Screening', 'Obesity',
       'Core preventive services for older women', 'Dental Visit',
       'Annual Checkup', 'Coronary Heart Disease', 'Mental Health',
       'High Blood Pressure', 'Physical Health', 'Binge Drinking',
       'Stroke', 'Arthritis', 'Colorectal Cancer Screening',
       'Core preventive services for older men'], dtype=object)
In [460]:
df_asthma.columns
Out[460]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [461]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [462]:
df_asthma.head()
Out[462]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text GEOID
1556830 2020 VA Virginia Portsmouth 51740 51740210400 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... 9.0 9.9 1490 POINT (-76.35541316 36.84059946) 51740210400 HLTHOUT CASTHMA CrdPrv Current Asthma 51740210400
2000046 2020 VA Virginia Fairfax 51059 51059432402 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... 7.6 8.2 4932 POINT (-77.26678987 38.76397301) 51059432402 HLTHOUT CASTHMA CrdPrv Current Asthma 51059432402
2000060 2020 VA Virginia Alleghany 51005 51005080302 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... 8.8 9.9 1553 POINT (-80.17249667 37.72153439) 51005080302 HLTHOUT CASTHMA CrdPrv Current Asthma 51005080302
2000093 2020 VA Virginia Arlington 51013 51013101703 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... 7.5 8.2 2324 POINT (-77.07487586 38.88942922) 51013101703 HLTHOUT CASTHMA CrdPrv Current Asthma 51013101703
2000102 2020 VA Virginia Fairfax 51059 51059451900 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... 8.1 8.8 6215 POINT (-77.15608334 38.82620478) 51059451900 HLTHOUT CASTHMA CrdPrv Current Asthma 51059451900

5 rows × 24 columns

In [463]:
gdf_US_CT['GEOID'] = gdf_US_CT['GEOID'].astype(str)
df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4026371282.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)
In [464]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [465]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [466]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[466]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2020 VA Virginia Portsmouth 51740 51740210400 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... G5020 S 20740701442676 Census Tract 2104 885758 656841 G5020 S 885758 656841
1 2020 VA Virginia Fairfax 51059 51059432402 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... G5020 S 207403714715612 Census Tract 4324.02 2891986 4330 G5020 S 2891986 4330
2 2020 VA Virginia Alleghany 51005 51005080302 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... G5020 S 20740812201001 Census Tract 803.02 309302859 1132328 G5020 S 309302859 1132328
3 2020 VA Virginia Arlington 51013 51013101703 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... G5020 S 207403710714771 Census Tract 1017.03 250441 0 G5020 S 250441 0
4 2020 VA Virginia Fairfax 51059 51059451900 BRFSS Health Outcomes Current asthma among adults aged >=18 years % ... G5020 S 207403714715697 Census Tract 4519 2793825 15602 G5020 S 2793825 15602

5 rows × 40 columns

In [467]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [468]:
df_2016_converted.head()
Out[468]:
GEOID_TRACT_20 Data_Value
0 51740210400 9.4
1 51059432402 7.9
2 51005080302 9.3
3 51013101703 7.8
4 51059451900 8.4
In [469]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [470]:
df_asthma = df_2016_converted
In [471]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [472]:
gdf_asthma.head()
Out[472]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 9.2
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 9.4
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 10.9
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 10.1
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 10.3
In [473]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [474]:
fig, ax = plt.subplots(figsize = (20,16)) 

# PLot the main map
gdf_asthma_subset.plot(column='Data_Value',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('Asthma - Crude Prevalence Rate', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
map_name = 'asthma_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [475]:
df_acs2021_var14 = gdf_asthma_subset
In [ ]:

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [476]:
df['Year'].unique()
Out[476]:
array([2020, 2019], dtype=int64)
In [477]:
df_diabetes=df[df['Measure']=='Diagnosed diabetes among adults aged >=18 years']
In [478]:
df_asthma = df_diabetes
In [479]:
df_asthma.shape
Out[479]:
(1876, 23)
In [480]:
df_asthma.shape
Out[480]:
(1876, 23)
In [481]:
df_asthma['Year'].unique()
Out[481]:
array([2020], dtype=int64)
In [482]:
df_asthma['CountyFIPS'].unique()
Out[482]:
array([51153, 51013, 51019, 51005, 51059, 51041, 51003, 51015, 51009,
       51027, 51029, 51025, 51021, 51033, 51001, 51043, 51035, 51031,
       51023, 51057, 51047, 51053, 51036, 51011, 51049, 51051, 51007,
       51017, 51037, 51045, 51149, 51107, 51141, 51073, 51119, 51101,
       51089, 51111, 51147, 51087, 51061, 51121, 51069, 51131, 51143,
       51117, 51125, 51105, 51067, 51085, 51079, 51115, 51099, 51093,
       51113, 51145, 51063, 51095, 51083, 51135, 51077, 51109, 51133,
       51137, 51075, 51065, 51155, 51139, 51103, 51097, 51081, 51127,
       51071, 51091, 51690, 51167, 51700, 51550, 51710, 51175, 51173,
       51179, 51183, 51540, 51680, 51510, 51670, 51161, 51650, 51177,
       51610, 51197, 51185, 51660, 51600, 51199, 51195, 51165, 51171,
       51187, 51595, 51683, 51191, 51163, 51169, 51520, 51570, 51159,
       51630, 51181, 51157, 51590, 51685, 51620, 51678, 51193, 51640,
       51580, 51530, 51810, 51740, 51770, 51760, 51820, 51800, 51790,
       51735, 51730, 51830, 51775, 51750, 51840, 51720], dtype=int64)
In [483]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[483]:
66
In [ ]:
In [484]:
df_asthma.columns
Out[484]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [485]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [486]:
df_asthma.head()
Out[486]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text GEOID
1622742 2020 VA Virginia Prince William 51153 51153900409 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... 10.9 12.0 4634 POINT (-77.32357954 38.62815634) 51153900409 HLTHOUT DIABETES CrdPrv Diabetes 51153900409
2000108 2020 VA Virginia Arlington 51013 51013101702 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... 4.4 4.9 4965 POINT (-77.07399411 38.89281841) 51013101702 HLTHOUT DIABETES CrdPrv Diabetes 51013101702
2000126 2020 VA Virginia Bedford 51019 51019030300 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... 11.1 12.2 3739 POINT (-79.45415334 37.48032111) 51019030300 HLTHOUT DIABETES CrdPrv Diabetes 51019030300
2000130 2020 VA Virginia Arlington 51013 51013101900 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... 5.2 5.9 2755 POINT (-77.09797008 38.87793399) 51013101900 HLTHOUT DIABETES CrdPrv Diabetes 51013101900
2000145 2020 VA Virginia Alleghany 51005 51005080201 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... 11.3 12.8 2842 POINT (-79.92599899 37.85000837) 51005080201 HLTHOUT DIABETES CrdPrv Diabetes 51005080201

5 rows × 24 columns

In [487]:
gdf_US_CT['GEOID'] = gdf_US_CT['GEOID'].astype(str)
df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4026371282.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)
In [488]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [489]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [490]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[490]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2020 VA Virginia Prince William 51153 51153900409 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... G5020 S 207403716692735 Census Tract 9004.09 2086522 0 G5020 S 2086522 0
1 2020 VA Virginia Arlington 51013 51013101702 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... G5020 S 207403710714772 Census Tract 1017.02 382463 0 G5020 S 99908 0
2 2020 VA Virginia Arlington 51013 51013101702 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... G5020 S 207403710714772 Census Tract 1017.02 382463 0 G5020 S 282555 0
3 2020 VA Virginia Bedford 51019 51019030300 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... G5020 S 207401025857449 Census Tract 303 337173831 1568719 G5020 S 337168402 1568719
4 2020 VA Virginia Bedford 51019 51019030300 BRFSS Health Outcomes Diagnosed diabetes among adults aged >=18 years % ... G5020 S 207401025857449 Census Tract 303 337173831 1568719 G5020 S 900 0

5 rows × 40 columns

In [491]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [492]:
df_2016_converted.head()
Out[492]:
GEOID_TRACT_20 Data_Value
0 51153900409 11.4
1 51013101704 4.6
2 51013101705 4.6
3 51019030300 11.7
4 51019030401 11.7
In [493]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [494]:
df_asthma = df_2016_converted
In [495]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [496]:
gdf_asthma.head()
Out[496]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 10.6
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 14.1
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 10.3
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 13.1
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 9.3
In [497]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [498]:
import matplotlib.pyplot as plt
import os

# Check and align CRS
if gdf_County_RVA.crs != gdf_asthma_subset.crs:
    gdf_County_RVA = gdf_County_RVA.to_crs(gdf_asthma_subset.crs)

# Create the plot
fig, ax = plt.subplots(figsize=(20, 16))

# Plot the choropleth map
gdf_asthma_subset.plot(column='Data_Value', cmap='YlOrRd', ax=ax, legend=True)

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('Diabetes - Crude Prevalence Rate', fontsize=20)

# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
save_dir = '.'  # Define save directory
map_name = 'diabetes_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [499]:
gdf_diabetes = gdf_asthma_subset
In [500]:
df_acs2021_var15 = gdf_diabetes
In [ ]:

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [501]:
df['Year'].unique()
Out[501]:
array([2020, 2019], dtype=int64)
In [502]:
df_HBP=df[df['Measure']=='High blood pressure among adults aged >=18 years']
In [503]:
df_asthma = df_HBP
In [504]:
df_asthma.shape
Out[504]:
(1876, 23)
In [505]:
df_asthma.shape
Out[505]:
(1876, 23)
In [506]:
df_asthma['Year'].unique()
Out[506]:
array([2019], dtype=int64)
In [507]:
df_asthma['CountyFIPS'].unique()
Out[507]:
array([51035, 51059, 51027, 51003, 51013, 51057, 51041, 51001, 51019,
       51025, 51021, 51029, 51005, 51009, 51015, 51043, 51033, 51031,
       51023, 51045, 51053, 51049, 51017, 51011, 51037, 51047, 51051,
       51036, 51007, 51069, 51083, 51085, 51121, 51081, 51109, 51061,
       51107, 51127, 51143, 51087, 51075, 51153, 51089, 51099, 51105,
       51079, 51117, 51101, 51135, 51141, 51115, 51077, 51145, 51065,
       51067, 51095, 51147, 51149, 51113, 51063, 51133, 51073, 51137,
       51155, 51103, 51093, 51119, 51125, 51091, 51139, 51097, 51071,
       51131, 51111, 51165, 51177, 51179, 51195, 51710, 51700, 51610,
       51199, 51650, 51630, 51540, 51550, 51570, 51510, 51191, 51185,
       51181, 51193, 51670, 51167, 51680, 51169, 51520, 51600, 51580,
       51173, 51683, 51161, 51690, 51197, 51183, 51175, 51660, 51157,
       51595, 51187, 51159, 51163, 51620, 51590, 51530, 51171, 51685,
       51640, 51678, 51770, 51760, 51740, 51810, 51800, 51720, 51820,
       51730, 51790, 51775, 51830, 51840, 51750, 51735], dtype=int64)
In [508]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[508]:
66
In [ ]:
In [509]:
df_asthma.columns
Out[509]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [510]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [511]:
df_asthma.head()
Out[511]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text GEOID
2000041 2019 VA Virginia Carroll 51035 51035080500 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... 40.8 42.6 4767 POINT (-80.5886884 36.73216479) 51035080500 HLTHOUT BPHIGH CrdPrv High Blood Pressure 51035080500
2000140 2019 VA Virginia Fairfax 51059 51059422403 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... 28.0 29.6 2695 POINT (-77.12279517 38.76996779) 51059422403 HLTHOUT BPHIGH CrdPrv High Blood Pressure 51059422403
2000153 2019 VA Virginia Buchanan 51027 51027010100 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... 40.9 43.3 5163 POINT (-82.02674711 37.35045817) 51027010100 HLTHOUT BPHIGH CrdPrv High Blood Pressure 51027010100
2000221 2019 VA Virginia Albemarle 51003 51003010202 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... 31.4 33.9 3077 POINT (-78.5118574 38.12235546) 51003010202 HLTHOUT BPHIGH CrdPrv High Blood Pressure 51003010202
2000248 2019 VA Virginia Arlington 51013 51013102003 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... 26.5 28.5 4370 POINT (-77.10660486 38.87191858) 51013102003 HLTHOUT BPHIGH CrdPrv High Blood Pressure 51013102003

5 rows × 24 columns

In [512]:
gdf_US_CT['GEOID'] = gdf_US_CT['GEOID'].astype(str)
df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4026371282.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)
In [513]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [514]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [515]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[515]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2019 VA Virginia Carroll 51035 51035080500 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... G5020 S 20740347126835 Census Tract 805 316593004 1323504 G5020 S 232908112 1074874
1 2019 VA Virginia Carroll 51035 51035080500 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... G5020 S 20740347126835 Census Tract 805 316593004 1323504 G5020 S 83684892 248630
2 2019 VA Virginia Fairfax 51059 51059422403 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... G5020 S 207403714715801 Census Tract 4224.03 2184365 38697 G5020 S 5354 0
3 2019 VA Virginia Fairfax 51059 51059422403 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... G5020 S 207403714715801 Census Tract 4224.03 2184365 38697 G5020 S 2179011 38697
4 2019 VA Virginia Buchanan 51027 51027010100 BRFSS Health Outcomes High blood pressure among adults aged >=18 years % ... G5020 S 20740763779598 Census Tract 101 233474425 421754 G5020 S 233470045 421754

5 rows × 40 columns

In [516]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [517]:
df_2016_converted.head()
Out[517]:
GEOID_TRACT_20 Data_Value
0 51035080501 41.7
1 51035080502 41.7
2 51059422301 28.7
3 51059422403 28.7
4 51027010100 42.1
In [518]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [519]:
df_asthma = df_2016_converted
In [520]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [521]:
gdf_asthma.head()
Out[521]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 36.1
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 41.1
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 35.1
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 39.1
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 32.0
In [522]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [523]:
import matplotlib.pyplot as plt
import os

# Check and align CRS
if gdf_County_RVA.crs != gdf_asthma_subset.crs:
    gdf_County_RVA = gdf_County_RVA.to_crs(gdf_asthma_subset.crs)

# Create the plot
fig, ax = plt.subplots(figsize=(20, 16))

# Plot the choropleth map
gdf_asthma_subset.plot(column='Data_Value', cmap='YlOrRd', ax=ax, legend=True)

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('High Blood Pressure - Crude Prevalence Rate', fontsize=20)

# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
save_dir = '.'  # Define save directory
map_name = 'HBP_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [ ]:
In [524]:
gdf_HBP = gdf_asthma_subset
In [525]:
df_acs2021_var16 = gdf_HBP
In [ ]:

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [526]:
df['Year'].unique()
Out[526]:
array([2020, 2019], dtype=int64)
In [527]:
df_Canc=df[df['Measure']=='Cancer (excluding skin cancer) among adults aged >=18 years']
In [528]:
df_asthma = df_Canc
In [529]:
df_asthma.shape
Out[529]:
(1876, 23)
In [530]:
df_asthma.shape
Out[530]:
(1876, 23)
In [531]:
df_asthma['Year'].unique()
Out[531]:
array([2020], dtype=int64)
In [532]:
df_asthma['CountyFIPS'].unique()
Out[532]:
array([51087, 51041, 51059, 51013, 51027, 51001, 51003, 51035, 51019,
       51005, 51047, 51036, 51009, 51025, 51021, 51053, 51031, 51015,
       51033, 51057, 51029, 51043, 51011, 51023, 51051, 51037, 51007,
       51045, 51017, 51049, 51153, 51107, 51069, 51111, 51089, 51077,
       51097, 51061, 51079, 51105, 51095, 51143, 51085, 51117, 51145,
       51099, 51149, 51121, 51083, 51133, 51141, 51067, 51137, 51101,
       51135, 51073, 51139, 51081, 51071, 51075, 51147, 51113, 51109,
       51065, 51155, 51119, 51093, 51127, 51115, 51103, 51131, 51063,
       51125, 51091, 51193, 51570, 51510, 51159, 51650, 51670, 51690,
       51177, 51199, 51161, 51191, 51710, 51550, 51171, 51179, 51165,
       51540, 51700, 51185, 51195, 51169, 51600, 51630, 51590, 51183,
       51680, 51167, 51520, 51580, 51175, 51530, 51197, 51157, 51173,
       51187, 51683, 51163, 51660, 51181, 51595, 51610, 51685, 51678,
       51620, 51640, 51810, 51800, 51760, 51740, 51820, 51735, 51750,
       51770, 51730, 51775, 51790, 51830, 51840, 51720], dtype=int64)
In [533]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[533]:
66
In [ ]:
In [534]:
df_asthma.columns
Out[534]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [535]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [536]:
df_asthma.head()
Out[536]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text GEOID
1622749 2020 VA Virginia Henrico 51087 51087200302 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... 6.0 6.7 3115 POINT (-77.51758409 37.58981841) 51087200302 HLTHOUT CANCER CrdPrv Cancer (except skin) 51087200302
2000031 2020 VA Virginia Chesterfield 51041 51041100816 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... 5.1 5.6 4636 POINT (-77.47115417 37.38558674) 51041100816 HLTHOUT CANCER CrdPrv Cancer (except skin) 51041100816
2000071 2020 VA Virginia Fairfax 51059 51059416100 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... 8.1 8.9 3734 POINT (-77.11440571 38.70934415) 51059416100 HLTHOUT CANCER CrdPrv Cancer (except skin) 51059416100
2000078 2020 VA Virginia Arlington 51013 51013102100 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... 7.6 8.3 2266 POINT (-77.12473187 38.86359765) 51013102100 HLTHOUT CANCER CrdPrv Cancer (except skin) 51013102100
2000112 2020 VA Virginia Fairfax 51059 51059415300 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... 5.9 6.4 3763 POINT (-77.0699354 38.76683047) 51059415300 HLTHOUT CANCER CrdPrv Cancer (except skin) 51059415300

5 rows × 24 columns

In [537]:
gdf_US_CT['GEOID'] = gdf_US_CT['GEOID'].astype(str)
df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4026371282.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)
In [538]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [539]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [540]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[540]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2020 VA Virginia Henrico 51087 51087200302 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... G5020 S 20740226602402 Census Tract 2003.02 2441640 6199 G5020 S 2441640 6199
1 2020 VA Virginia Chesterfield 51041 51041100816 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... G5020 S 20740355748280 Census Tract 1008.16 4328452 33784 G5020 S 4328452 33784
2 2020 VA Virginia Fairfax 51059 51059416100 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... G5020 S 20740241118385 Census Tract 4161 5502308 1896018 G5020 S 5502308 1891463
3 2020 VA Virginia Fairfax 51059 51059416100 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... G5020 S 20740241118385 Census Tract 4161 5502308 1896018 G5020 S 0 4555
4 2020 VA Virginia Arlington 51013 51013102100 BRFSS Health Outcomes Cancer (excluding skin cancer) among adults ag... % ... G5020 S 20740343189396 Census Tract 1021 1155864 0 G5020 S 4386 0

5 rows × 40 columns

In [541]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [542]:
df_2016_converted.head()
Out[542]:
GEOID_TRACT_20 Data_Value
0 51087200302 6.3
1 51041100816 5.4
2 51059416100 8.5
3 51059416200 8.5
4 51013101200 7.9
In [543]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [544]:
df_asthma = df_2016_converted
In [545]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [546]:
gdf_asthma.head()
Out[546]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 7.0
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 6.6
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 6.7
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 5.1
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 5.3
In [547]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [548]:
import matplotlib.pyplot as plt
import os

# Check and align CRS
if gdf_County_RVA.crs != gdf_asthma_subset.crs:
    gdf_County_RVA = gdf_County_RVA.to_crs(gdf_asthma_subset.crs)

# Create the plot
fig, ax = plt.subplots(figsize=(20, 16))

# Plot the choropleth map
gdf_asthma_subset.plot(column='Data_Value', cmap='YlOrRd', ax=ax, legend=True)

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('Cancer - Crude Prevalence Rate', fontsize=20)

# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
save_dir = '.'  # Define save directory
map_name = 'cancer_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [ ]:
In [549]:
gdf_Canc = gdf_asthma_subset
In [550]:
df_acs2021_var17 = gdf_Canc
In [ ]:

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [551]:
df['Year'].unique()
Out[551]:
array([2020, 2019], dtype=int64)
In [552]:
df_Obesity=df[df['Measure']=='Obesity among adults aged >=18 years']
In [553]:
df_asthma = df_Obesity
In [554]:
df_asthma.shape
Out[554]:
(1876, 23)
In [555]:
df_asthma.shape
Out[555]:
(1876, 23)
In [556]:
df_asthma['Year'].unique()
Out[556]:
array([2020], dtype=int64)
In [557]:
df_asthma['CountyFIPS'].unique()
Out[557]:
array([51153, 51009, 51059, 51047, 51003, 51035, 51015, 51013, 51041,
       51057, 51025, 51019, 51027, 51029, 51036, 51051, 51023, 51007,
       51053, 51001, 51037, 51031, 51005, 51011, 51049, 51033, 51043,
       51021, 51087, 51017, 51045, 51640, 51077, 51141, 51085, 51061,
       51093, 51095, 51121, 51099, 51145, 51111, 51069, 51107, 51117,
       51101, 51143, 51133, 51127, 51119, 51089, 51147, 51105, 51109,
       51067, 51073, 51083, 51125, 51065, 51139, 51097, 51079, 51135,
       51063, 51155, 51131, 51071, 51075, 51137, 51149, 51103, 51081,
       51113, 51091, 51115, 51710, 51169, 51700, 51187, 51510, 51580,
       51179, 51161, 51595, 51570, 51550, 51680, 51191, 51171, 51177,
       51193, 51165, 51650, 51540, 51670, 51683, 51173, 51660, 51175,
       51590, 51197, 51195, 51199, 51185, 51678, 51690, 51167, 51685,
       51600, 51183, 51163, 51520, 51610, 51620, 51630, 51181, 51159,
       51157, 51530, 51800, 51760, 51810, 51740, 51840, 51770, 51790,
       51735, 51775, 51820, 51830, 51730, 51750, 51720], dtype=int64)
In [558]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[558]:
66
In [ ]:
In [559]:
df_asthma.columns
Out[559]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [560]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [561]:
df_asthma.head()
Out[561]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text GEOID
1622752 2020 VA Virginia Prince William 51153 51153901229 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... 35.2 37.5 3888 POINT (-77.38157193 38.65151257) 51153901229 HLTHOUT OBESITY CrdPrv Obesity 51153901229
2000049 2020 VA Virginia Amherst 51009 51009010401 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... 35.1 37.0 3375 POINT (-79.09134424 37.50204912) 51009010401 HLTHOUT OBESITY CrdPrv Obesity 51009010401
2000055 2020 VA Virginia Fairfax 51059 51059480203 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... 18.7 20.4 2054 POINT (-77.23544376 38.92756268) 51059480203 HLTHOUT OBESITY CrdPrv Obesity 51059480203
2000063 2020 VA Virginia Culpeper 51047 51047930202 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... 37.3 39.6 5820 POINT (-78.11197556 38.51815056) 51047930202 HLTHOUT OBESITY CrdPrv Obesity 51047930202
2000087 2020 VA Virginia Fairfax 51059 51059471201 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... 18.7 20.3 3164 POINT (-77.21376986 38.91742756) 51059471201 HLTHOUT OBESITY CrdPrv Obesity 51059471201

5 rows × 24 columns

In [562]:
gdf_US_CT['GEOID'] = gdf_US_CT['GEOID'].astype(str)
df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4026371282.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)
In [563]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [564]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [565]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[565]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2020 VA Virginia Prince William 51153 51153901229 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... G5020 S 207403716693560 Census Tract 9012.29 2554838 241 G5020 S 2554838 241
1 2020 VA Virginia Amherst 51009 51009010401 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... G5020 S 20740744624938 Census Tract 104.01 31658976 99682 G5020 S 31658976 99682
2 2020 VA Virginia Fairfax 51059 51059480203 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... G5020 S 207403714715945 Census Tract 4802.03 372822 3133 G5020 S 372822 3133
3 2020 VA Virginia Culpeper 51047 51047930202 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... G5020 S 207403690794086 Census Tract 9302.02 145295164 1271946 G5020 S 145294726 1271946
4 2020 VA Virginia Culpeper 51047 51047930202 BRFSS Health Outcomes Obesity among adults aged >=18 years % ... G5020 S 207403690794086 Census Tract 9302.02 145295164 1271946 G5020 S 438 0

5 rows × 40 columns

In [566]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [567]:
df_2016_converted.head()
Out[567]:
GEOID_TRACT_20 Data_Value
0 51153901229 36.4
1 51009010401 36.1
2 51059480203 19.5
3 51047930202 38.4
4 51113930202 38.4
In [568]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [569]:
df_asthma = df_2016_converted
In [570]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [571]:
gdf_asthma.head()
Out[571]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 37.7
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 38.0
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 41.0
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 39.7
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 40.8
In [572]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [573]:
import matplotlib.pyplot as plt
import os

# Check and align CRS
if gdf_County_RVA.crs != gdf_asthma_subset.crs:
    gdf_County_RVA = gdf_County_RVA.to_crs(gdf_asthma_subset.crs)

# Create the plot
fig, ax = plt.subplots(figsize=(20, 16))

# Plot the choropleth map
gdf_asthma_subset.plot(column='Data_Value', cmap='YlOrRd', ax=ax, legend=True)

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('Obesity - Crude Prevalence Rate', fontsize=20)

# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
save_dir = '.'  # Define save directory
map_name = 'Obesity_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [ ]:
In [574]:
gdf_Obesity = gdf_asthma_subset
In [575]:
df_acs2021_var18 = gdf_Obesity
In [ ]:

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [576]:
df['Year'].unique()
Out[576]:
array([2020, 2019], dtype=int64)
In [577]:
df_heart=df[df['Measure']=='Coronary heart disease among adults aged >=18 years']
In [578]:
df_asthma = df_heart
In [579]:
df_asthma.shape
Out[579]:
(1876, 23)
In [580]:
df_asthma.shape
Out[580]:
(1876, 23)
In [581]:
df_asthma['Year'].unique()
Out[581]:
array([2020], dtype=int64)
In [582]:
df_asthma['CountyFIPS'].unique()
Out[582]:
array([51059, 51053, 51041, 51023, 51031, 51015, 51003, 51013, 51007,
       51027, 51009, 51001, 51019, 51025, 51049, 51051, 51029, 51057,
       51047, 51037, 51036, 51005, 51035, 51011, 51033, 51021, 51043,
       51017, 51045, 51153, 51089, 51143, 51107, 51061, 51095, 51067,
       51093, 51139, 51087, 51073, 51149, 51109, 51155, 51085, 51081,
       51083, 51141, 51119, 51101, 51077, 51069, 51127, 51065, 51121,
       51105, 51145, 51075, 51117, 51063, 51115, 51125, 51079, 51097,
       51099, 51135, 51137, 51133, 51131, 51071, 51113, 51147, 51103,
       51111, 51091, 51710, 51177, 51191, 51650, 51171, 51590, 51179,
       51195, 51670, 51175, 51700, 51187, 51680, 51683, 51660, 51197,
       51510, 51167, 51550, 51163, 51165, 51169, 51595, 51181, 51185,
       51199, 51630, 51620, 51161, 51540, 51183, 51173, 51685, 51193,
       51690, 51600, 51520, 51159, 51570, 51530, 51610, 51640, 51580,
       51157, 51678, 51800, 51760, 51790, 51770, 51810, 51740, 51720,
       51730, 51735, 51775, 51820, 51750, 51840, 51830], dtype=int64)
In [583]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[583]:
66
In [ ]:
In [584]:
df_asthma.columns
Out[584]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [585]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [586]:
df_asthma.head()
Out[586]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text GEOID
2000033 2020 VA Virginia Fairfax 51059 51059451601 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... 3.8 4.5 6047 POINT (-77.13897812 38.85027183) 51059451601 HLTHOUT CHD CrdPrv Coronary Heart Disease 51059451601
2000050 2020 VA Virginia Fairfax 51059 51059450702 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... 3.9 4.5 4399 POINT (-77.20341412 38.83710525) 51059450702 HLTHOUT CHD CrdPrv Coronary Heart Disease 51059450702
2000121 2020 VA Virginia Dinwiddie 51053 51053840400 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... 7.5 8.7 1079 POINT (-77.45617348 37.21184905) 51053840400 HLTHOUT CHD CrdPrv Coronary Heart Disease 51053840400
2000166 2020 VA Virginia Chesterfield 51041 51041100403 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... 4.7 5.5 5509 POINT (-77.34313994 37.36779408) 51041100403 HLTHOUT CHD CrdPrv Coronary Heart Disease 51041100403
2000171 2020 VA Virginia Fairfax 51059 51059451000 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... 5.1 6.5 2711 POINT (-77.18217079 38.84251901) 51059451000 HLTHOUT CHD CrdPrv Coronary Heart Disease 51059451000

5 rows × 24 columns

In [587]:
gdf_US_CT['GEOID'] = gdf_US_CT['GEOID'].astype(str)
df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4026371282.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)
In [588]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [589]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [590]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[590]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2020 VA Virginia Fairfax 51059 51059451601 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... G5020 S 207403714715695 Census Tract 4516.01 949593 0 G5020 S 949593 0
1 2020 VA Virginia Fairfax 51059 51059450702 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... G5020 S 207403714715648 Census Tract 4507.02 1239948 0 G5020 S 1239948 0
2 2020 VA Virginia Dinwiddie 51053 51053840400 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... G5020 S 20740357530278 Census Tract 8404 5209925 121744 G5020 S 5209925 121744
3 2020 VA Virginia Chesterfield 51041 51041100403 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... G5020 S 20740355747877 Census Tract 1004.03 61241844 10191570 G5020 S 61241844 10191570
4 2020 VA Virginia Fairfax 51059 51059451000 BRFSS Health Outcomes Coronary heart disease among adults aged >=18 ... % ... G5020 S 20740241117997 Census Tract 4510 1742349 4079 G5020 S 1742349 4079

5 rows × 40 columns

In [591]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [592]:
df_2016_converted.head()
Out[592]:
GEOID_TRACT_20 Data_Value
0 51059451601 4.1
1 51059450702 4.2
2 51053840400 8.1
3 51041100403 5.0
4 51059451000 5.7
In [593]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [594]:
df_asthma = df_2016_converted
In [595]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [596]:
gdf_asthma.head()
Out[596]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 5.9
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 6.1
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 6.5
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 4.8
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 5.8
In [597]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [598]:
import matplotlib.pyplot as plt
import os

# Check and align CRS
if gdf_County_RVA.crs != gdf_asthma_subset.crs:
    gdf_County_RVA = gdf_County_RVA.to_crs(gdf_asthma_subset.crs)

# Create the plot
fig, ax = plt.subplots(figsize=(20, 16))

# Plot the choropleth map
gdf_asthma_subset.plot(column='Data_Value', cmap='YlOrRd', ax=ax, legend=True)

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('Heart Problems - Crude Prevalence Rate', fontsize=20)

# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
save_dir = '.'  # Define save directory
map_name = 'Heart_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [ ]:
In [599]:
gdf_heart = gdf_asthma_subset
In [600]:
df_acs2021_var19 = gdf_heart
In [ ]:

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [601]:
df['Year'].unique()
Out[601]:
array([2020, 2019], dtype=int64)
In [602]:
df_stroke=df[df['Measure']=='Stroke among adults aged >=18 years']
In [603]:
df_asthma = df_stroke
In [604]:
df_asthma.shape
Out[604]:
(1876, 23)
In [605]:
df_asthma.shape
Out[605]:
(1876, 23)
In [606]:
df_asthma['Year'].unique()
Out[606]:
array([2020], dtype=int64)
In [607]:
df_asthma['CountyFIPS'].unique()
Out[607]:
array([51041, 51059, 51013, 51027, 51025, 51057, 51033, 51003, 51031,
       51019, 51047, 51015, 51029, 51005, 51035, 51001, 51011, 51043,
       51009, 51023, 51021, 51036, 51037, 51017, 51053, 51049, 51007,
       51051, 51063, 51155, 51077, 51045, 51087, 51067, 51095, 51107,
       51191, 51595, 51710, 51139, 51153, 51083, 51111, 51121, 51143,
       51131, 51085, 51105, 51149, 51093, 51101, 51145, 51073, 51075,
       51061, 51109, 51115, 51147, 51117, 51069, 51089, 51119, 51141,
       51099, 51103, 51071, 51133, 51065, 51135, 51113, 51137, 51127,
       51081, 51125, 51091, 51097, 51079, 51550, 51580, 51197, 51157,
       51690, 51165, 51650, 51680, 51510, 51173, 51175, 51540, 51630,
       51177, 51179, 51700, 51199, 51590, 51185, 51570, 51195, 51660,
       51167, 51520, 51171, 51187, 51183, 51163, 51161, 51610, 51640,
       51670, 51530, 51169, 51193, 51678, 51683, 51685, 51620, 51600,
       51159, 51181, 51760, 51800, 51810, 51740, 51770, 51830, 51730,
       51820, 51790, 51840, 51720, 51775, 51750, 51735], dtype=int64)
In [608]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[608]:
66
In [ ]:
In [609]:
df_asthma.columns
Out[609]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [610]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [611]:
df_asthma.head()
Out[611]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text GEOID
2000080 2020 VA Virginia Chesterfield 51041 51041100912 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... 2.0 2.6 6268 POINT (-77.6528554 37.43550455) 51041100912 HLTHOUT STROKE CrdPrv Stroke 51041100912
2000081 2020 VA Virginia Fairfax 51059 51059470100 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... 1.8 2.4 2732 POINT (-77.16120606 38.95507591) 51059470100 HLTHOUT STROKE CrdPrv Stroke 51059470100
2000086 2020 VA Virginia Fairfax 51059 51059481105 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... 1.3 1.7 5539 POINT (-77.39815572 38.9361009) 51059481105 HLTHOUT STROKE CrdPrv Stroke 51059481105
2000088 2020 VA Virginia Chesterfield 51041 51041100910 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... 2.3 2.9 5351 POINT (-77.5832998 37.4750995) 51041100910 HLTHOUT STROKE CrdPrv Stroke 51041100910
2000150 2020 VA Virginia Fairfax 51059 51059431802 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... 1.8 2.3 3216 POINT (-77.26866062 38.80826451) 51059431802 HLTHOUT STROKE CrdPrv Stroke 51059431802

5 rows × 24 columns

In [612]:
gdf_US_CT['GEOID'] = gdf_US_CT['GEOID'].astype(str)
df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4026371282.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)
In [613]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [614]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [615]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[615]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2020 VA Virginia Chesterfield 51041 51041100912 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... G5020 S 20740355746822 Census Tract 1009.12 7964559 2550215 G5020 S 7964559 2550215
1 2020 VA Virginia Fairfax 51059 51059470100 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... G5020 S 20740241117483 Census Tract 4701 7577922 4027 G5020 S 7577922 4027
2 2020 VA Virginia Fairfax 51059 51059481105 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... G5020 S 207403714715987 Census Tract 4811.05 3370314 15228 G5020 S 3370314 15228
3 2020 VA Virginia Chesterfield 51041 51041100910 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... G5020 S 20740355746793 Census Tract 1009.10 6543271 22977 G5020 S 6543271 22977
4 2020 VA Virginia Fairfax 51059 51059431802 BRFSS Health Outcomes Stroke among adults aged >=18 years % ... G5020 S 207403714715570 Census Tract 4318.02 1912912 6174 G5020 S 1912912 6174

5 rows × 40 columns

In [616]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [617]:
df_2016_converted.head()
Out[617]:
GEOID_TRACT_20 Data_Value
0 51041100912 2.3
1 51059470100 2.1
2 51059481105 1.5
3 51041100910 2.6
4 51059431802 2.0
In [618]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [619]:
df_asthma = df_2016_converted
In [620]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [621]:
gdf_asthma.head()
Out[621]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 2.9
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 2.9
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 3.9
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 2.6
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 2.6
In [622]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [623]:
import matplotlib.pyplot as plt
import os

# Check and align CRS
if gdf_County_RVA.crs != gdf_asthma_subset.crs:
    gdf_County_RVA = gdf_County_RVA.to_crs(gdf_asthma_subset.crs)

# Create the plot
fig, ax = plt.subplots(figsize=(20, 16))

# Plot the choropleth map
gdf_asthma_subset.plot(column='Data_Value', cmap='YlOrRd', ax=ax, legend=True)

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('Stroke - Crude Prevalence Rate', fontsize=20)

# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
save_dir = '.'  # Define save directory
map_name = 'Stroke_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [ ]:
In [624]:
gdf_stroke = gdf_asthma_subset
In [625]:
df_acs2021_var20 = gdf_stroke
In [ ]:

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [626]:
df['Year'].unique()
Out[626]:
array([2020, 2019], dtype=int64)
In [627]:
df_MH=df[df['Measure']=='Mental health not good for >=14 days among adults aged >=18 years']
In [628]:
df_asthma = df_MH
In [629]:
df_asthma.shape
Out[629]:
(1876, 23)
In [630]:
df_asthma.shape
Out[630]:
(1876, 23)
In [631]:
df_asthma['Year'].unique()
Out[631]:
array([2020], dtype=int64)
In [632]:
df_asthma['CountyFIPS'].unique()
Out[632]:
array([51059, 51041, 51003, 51023, 51001, 51045, 51031, 51013, 51035,
       51011, 51015, 51036, 51033, 51053, 51027, 51009, 51047, 51029,
       51057, 51021, 51025, 51019, 51043, 51005, 51051, 51037, 51017,
       51007, 51049, 51145, 51087, 51093, 51079, 51137, 51085, 51063,
       51153, 51107, 51069, 51139, 51067, 51075, 51141, 51077, 51099,
       51121, 51071, 51117, 51113, 51149, 51133, 51155, 51143, 51095,
       51109, 51061, 51127, 51119, 51073, 51065, 51081, 51089, 51083,
       51147, 51103, 51131, 51101, 51111, 51091, 51105, 51115, 51125,
       51097, 51135, 51690, 51510, 51179, 51710, 51590, 51165, 51650,
       51173, 51680, 51185, 51177, 51700, 51540, 51550, 51610, 51161,
       51195, 51191, 51620, 51570, 51199, 51171, 51630, 51167, 51183,
       51595, 51670, 51175, 51159, 51163, 51640, 51197, 51520, 51187,
       51660, 51683, 51600, 51157, 51169, 51193, 51580, 51685, 51181,
       51678, 51530, 51790, 51735, 51810, 51760, 51800, 51730, 51770,
       51830, 51840, 51740, 51750, 51820, 51775, 51720], dtype=int64)
In [633]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[633]:
66
In [ ]:
In [634]:
df_asthma.columns
Out[634]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [635]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [636]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [637]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [638]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[638]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2020 VA Virginia Fairfax 51059 51059481101 BRFSS Health Status Mental health not good for >=14 days among adu... % ... G5020 S 207403714716012 Census Tract 4811.01 854167 19712 G5020 S 832920 19712
1 2020 VA Virginia Fairfax 51059 51059481101 BRFSS Health Status Mental health not good for >=14 days among adu... % ... G5020 S 207403714716012 Census Tract 4811.01 854167 19712 G5020 S 10343 0
2 2020 VA Virginia Fairfax 51059 51059481101 BRFSS Health Status Mental health not good for >=14 days among adu... % ... G5020 S 207403714716012 Census Tract 4811.01 854167 19712 G5020 S 10904 0
3 2020 VA Virginia Fairfax 51059 51059422102 BRFSS Health Status Mental health not good for >=14 days among adu... % ... G5020 S 207403714715984 Census Tract 4221.02 6768650 114990 G5020 S 0 1388
4 2020 VA Virginia Fairfax 51059 51059422102 BRFSS Health Status Mental health not good for >=14 days among adu... % ... G5020 S 207403714715984 Census Tract 4221.02 6768650 114990 G5020 S 6647091 113602

5 rows × 40 columns

In [639]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [640]:
df_2016_converted.head()
Out[640]:
GEOID_TRACT_20 Data_Value
0 51059481101 11.5
1 51059481201 11.5
2 51059482506 11.5
3 51059416300 13.6
4 51059422102 13.6
In [641]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [642]:
df_asthma = df_2016_converted
In [643]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [644]:
gdf_asthma.head()
Out[644]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 13.0
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 14.6
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 14.0
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 12.7
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 15.6
In [645]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [646]:
import matplotlib.pyplot as plt
import os

# Check and align CRS
if gdf_County_RVA.crs != gdf_asthma_subset.crs:
    gdf_County_RVA = gdf_County_RVA.to_crs(gdf_asthma_subset.crs)

# Create the plot
fig, ax = plt.subplots(figsize=(20, 16))

# Plot the choropleth map
gdf_asthma_subset.plot(column='Data_Value', cmap='YlOrRd', ax=ax, legend=True)

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('Mental Health Issues - Crude Prevalence Rate', fontsize=20)

# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
save_dir = '.'  # Define save directory
map_name = 'MH_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [ ]:
In [647]:
gdf_MH = gdf_asthma_subset
In [648]:
df_acs2021_var21 = gdf_MH
In [ ]:

Useable Variables¶

  1. 'Current asthma among adults aged >=18 years'
  2. 'Diagnosed diabetes among adults aged >=18 years'
  3. 'High blood pressure among adults aged >=18 years'
  4. 'Current lack of health insurance among adults aged 18-64 years'
  5. 'Cancer (excluding skin cancer) among adults aged >=18 years'
  6. 'Obesity among adults aged >=18 years'
  7. 'Coronary heart disease among adults aged >=18 years'
  8. 'Stroke among adults aged >=18 years'
  9. 'Mental health not good for >=14 days among adults aged >=18 years'
  10. 'Chronic obstructive pulmonary disease among adults aged >=18 years'
In [649]:
df['Year'].unique()
Out[649]:
array([2020, 2019], dtype=int64)
In [650]:
df_COPD=df[df['Measure']=='Chronic obstructive pulmonary disease among adults aged >=18 years']
In [651]:
df_asthma = df_COPD
In [652]:
df_asthma.shape
Out[652]:
(1876, 23)
In [653]:
df_asthma.shape
Out[653]:
(1876, 23)
In [654]:
df_asthma['Year'].unique()
Out[654]:
array([2020], dtype=int64)
In [655]:
df_asthma['CountyFIPS'].unique()
Out[655]:
array([51105, 51059, 51013, 51009, 51027, 51041, 51015, 51051, 51003,
       51019, 51029, 51053, 51047, 51031, 51011, 51023, 51001, 51035,
       51005, 51049, 51057, 51033, 51037, 51017, 51025, 51043, 51007,
       51021, 51036, 51045, 51115, 51109, 51087, 51153, 51141, 51099,
       51077, 51121, 51061, 51079, 51069, 51089, 51133, 51119, 51107,
       51143, 51085, 51093, 51113, 51065, 51131, 51111, 51155, 51081,
       51063, 51137, 51125, 51127, 51083, 51103, 51095, 51067, 51097,
       51135, 51139, 51117, 51073, 51149, 51075, 51145, 51147, 51071,
       51101, 51091, 51650, 51550, 51595, 51193, 51700, 51510, 51165,
       51187, 51660, 51199, 51177, 51171, 51179, 51159, 51710, 51175,
       51197, 51680, 51191, 51590, 51161, 51169, 51620, 51163, 51670,
       51683, 51195, 51570, 51600, 51540, 51181, 51173, 51185, 51167,
       51685, 51580, 51157, 51520, 51183, 51630, 51610, 51690, 51678,
       51530, 51640, 51810, 51750, 51740, 51760, 51730, 51770, 51800,
       51820, 51790, 51775, 51840, 51735, 51720, 51830], dtype=int64)
In [656]:
len(df_asthma[df_asthma['CountyFIPS']==51760].sort_values(by='LocationName', ascending=True))
Out[656]:
66
In [ ]:
In [657]:
df_asthma.columns
Out[657]:
Index(['Year', 'StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS',
       'LocationName', 'DataSource', 'Category', 'Measure', 'Data_Value_Unit',
       'Data_Value_Type', 'Data_Value', 'Data_Value_Footnote_Symbol',
       'Data_Value_Footnote', 'Low_Confidence_Limit', 'High_Confidence_Limit',
       'TotalPopulation', 'Geolocation', 'LocationID', 'CategoryID',
       'MeasureId', 'DataValueTypeID', 'Short_Question_Text'],
      dtype='object')
In [658]:
df_asthma['GEOID'] = df_asthma['LocationName']

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4020978062.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['LocationName']
In [659]:
df_asthma.head()
Out[659]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... Low_Confidence_Limit High_Confidence_Limit TotalPopulation Geolocation LocationID CategoryID MeasureId DataValueTypeID Short_Question_Text GEOID
1622740 2020 VA Virginia Lee 51105 51105950600 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... 10.0 12.5 4180 POINT (-83.44998086 36.6319259) 51105950600 HLTHOUT COPD CrdPrv COPD 51105950600
2000038 2020 VA Virginia Fairfax 51059 51059480501 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... 2.8 4.1 3604 POINT (-77.36128253 38.9996284) 51059480501 HLTHOUT COPD CrdPrv COPD 51059480501
2000076 2020 VA Virginia Fairfax 51059 51059480802 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... 2.4 3.5 3685 POINT (-77.37273206 38.97235733) 51059480802 HLTHOUT COPD CrdPrv COPD 51059480802
2000091 2020 VA Virginia Fairfax 51059 51059432701 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... 3.0 4.4 3210 POINT (-77.2195351 38.74759246) 51059432701 HLTHOUT COPD CrdPrv COPD 51059432701
2000148 2020 VA Virginia Arlington 51013 51013103503 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... 1.8 2.5 4109 POINT (-77.05341025 38.86081883) 51013103503 HLTHOUT COPD CrdPrv COPD 51013103503

5 rows × 24 columns

In [660]:
gdf_US_CT['GEOID'] = gdf_US_CT['GEOID'].astype(str)
df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\4026371282.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID'] = df_asthma['GEOID'].astype(str)
In [661]:
import pandas as pd

# Load the crosswalk file
crosswalk_path = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\crosswalk_CT10to20.txt"
# df_crosswalk = pd.read_csv(crosswalk_path, delimiter='\t', dtype=str)
df_crosswalk = pd.read_csv(crosswalk_path, delimiter='|', dtype=str)
# Display the first few rows of the crosswalk DataFrame
print(df_crosswalk.head())

     OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20  \
0  20790540092527    01001020100  Census Tract 201           9825304   
1  20790540092527    01001020100  Census Tract 201           9825304   
2  20790540092534    01001020200  Census Tract 202           3320818   
3  20790540092528    01001020300  Census Tract 203           5349271   
4  20790540092529    01001020400  Census Tract 204           6384282   

  AREAWATER_TRACT_20 MTFCC_TRACT_20 FUNCSTAT_TRACT_20    OID_TRACT_10  \
0              28435          G5020                 S  20740540092527   
1              28435          G5020                 S  20740540092534   
2               5669          G5020                 S  20740540092534   
3               9054          G5020                 S  20740540092528   
4               8408          G5020                 S  20740540092529   

  GEOID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10  \
0    01001020100  Census Tract 201           9827271              28435   
1    01001020200  Census Tract 202           3325674               5669   
2    01001020200  Census Tract 202           3325674               5669   
3    01001020300  Census Tract 203           5349271               9054   
4    01001020400  Census Tract 204           6384282               8408   

  MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART  
0          G5020                 S       9820448          28435  
1          G5020                 S          4856              0  
2          G5020                 S       3320818           5669  
3          G5020                 S       5349271           9054  
4          G5020                 S       6384282           8408
In [662]:
# Ensure 'GEOID_2016' is a string for merging
df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\3898980032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asthma['GEOID_TRACT_10'] = df_asthma['GEOID'].astype(str)
In [663]:
df_2016_merged = pd.merge(df_asthma, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[663]:
Year StateAbbr StateDesc CountyName CountyFIPS LocationName DataSource Category Measure Data_Value_Unit ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 2020 VA Virginia Lee 51105 51105950600 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... G5020 S 20740252582248 Census Tract 9506 226468228 428131 G5020 S 226468228 428131
1 2020 VA Virginia Fairfax 51059 51059480501 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... G5020 S 207403714715772 Census Tract 4805.01 5040081 54976 G5020 S 5040081 54976
2 2020 VA Virginia Fairfax 51059 51059480802 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... G5020 S 207403714715780 Census Tract 4808.02 2592216 5857 G5020 S 2587389 5857
3 2020 VA Virginia Fairfax 51059 51059480802 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... G5020 S 207403714715780 Census Tract 4808.02 2592216 5857 G5020 S 4827 0
4 2020 VA Virginia Fairfax 51059 51059432701 BRFSS Health Outcomes Chronic obstructive pulmonary disease among ad... % ... G5020 S 207403714715486 Census Tract 4327.01 2024616 2108 G5020 S 2024616 2108

5 rows × 40 columns

In [664]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20', 'Data_Value']]
In [665]:
df_2016_converted.head()
Out[665]:
GEOID_TRACT_20 Data_Value
0 51105950600 11.2
1 51059480501 3.4
2 51059480802 2.9
3 51059480902 2.9
4 51059432701 3.6
In [666]:
df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\769660856.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID']=df_2016_converted['GEOID_TRACT_20'].astype(str)
In [667]:
df_asthma = df_2016_converted
In [668]:
gdf_asthma = pd.merge(gdf_US_CT,df_asthma, on = 'GEOID', how = 'outer')
In [669]:
gdf_asthma.head()
Out[669]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry GEOID_TRACT_20 Data_Value
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534.0 2927606.0 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 51085320100 6.3
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 6.9
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521.0 16144.0 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 51085321201 7.7
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 6.9
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075.0 96758.0 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 51085321202 4.0
In [670]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [671]:
import matplotlib.pyplot as plt
import os

# Check and align CRS
if gdf_County_RVA.crs != gdf_asthma_subset.crs:
    gdf_County_RVA = gdf_County_RVA.to_crs(gdf_asthma_subset.crs)

# Create the plot
fig, ax = plt.subplots(figsize=(20, 16))

# Plot the choropleth map
gdf_asthma_subset.plot(column='Data_Value', cmap='YlOrRd', ax=ax, legend=True)

# Plot the county boundaries
gdf_County_RVA.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent

# Add Title and Labels
ax.set_title('COPD - Crude Prevalence Rate', fontsize=20)

# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

# Save the map
save_dir = '.'  # Define save directory
map_name = 'COPD_High_Confidence_Limit.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [ ]:
In [672]:
gdf_COPD = gdf_asthma_subset
In [673]:
df_acs2021_var22 = gdf_COPD
In [ ]:
In [ ]:
In [674]:
# Var 23: Energy Burden
In [675]:
pd.set_option("display.max_rows",None)
In [ ]:
In [676]:
import pandas as pd
In [677]:
df_acs2021.rename (columns = {'B06009_001E':'PopGr25'}, inplace = True)
In [678]:
df_energy = pd.read_excel(r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\Energy_Burden\Energy_Cost_by_CT.xlsx")
In [ ]:
In [679]:
df_energy.rename(columns = {'Row Labels':'GEOID'}, inplace=True)
In [680]:
# Ensure 'GEOID_2016' is a string for merging
df_energy['GEOID_TRACT_10'] = df_energy['GEOID'].astype(str)
In [681]:
df_2016_merged = pd.merge(df_energy, df_crosswalk, on='GEOID_TRACT_10', how='inner')

df_2016_merged.head()
Out[681]:
GEOID Average of ELEP Average of GASP Average of FULP Total_Energy_Cost GEOID_TRACT_10 OID_TRACT_20 GEOID_TRACT_20 NAMELSAD_TRACT_20 AREALAND_TRACT_20 ... MTFCC_TRACT_20 FUNCSTAT_TRACT_20 OID_TRACT_10 NAMELSAD_TRACT_10 AREALAND_TRACT_10 AREAWATER_TRACT_10 MTFCC_TRACT_10 FUNCSTAT_TRACT_10 AREALAND_PART AREAWATER_PART
0 51001090100 3711.022654 786.045389 286.108557 4783.176599 51001090100 2079015501440908 51001090101 Census Tract 901.01 11889599 ... G5020 S 207403716859403 Census Tract 901 18542572 37914864 G5020 S 11889599 32123347
1 51001090100 3711.022654 786.045389 286.108557 4783.176599 51001090100 2079015501440904 51001090102 Census Tract 901.02 6652973 ... G5020 S 207403716859403 Census Tract 901 18542572 37914864 G5020 S 6652973 5791517
2 51001090200 2518.318496 897.841616 296.482416 3712.642528 51001090200 2079015501440899 51001090201 Census Tract 902.01 67750622 ... G5020 S 207403716859380 Census Tract 902 187803123 36546258 G5020 S 67750622 33782725
3 51001090200 2518.318496 897.841616 296.482416 3712.642528 51001090200 2079015501440876 51001090202 Census Tract 902.02 120052501 ... G5020 S 207403716859380 Census Tract 902 187803123 36546258 G5020 S 120052501 2763533
4 51001090300 1991.246836 811.742204 273.838732 3076.827772 51001090300 20790353205714 51001090300 Census Tract 903 128419397 ... G5020 S 20740353205714 Census Tract 903 128419397 61780185 G5020 S 128419397 61780185

5 rows × 21 columns

In [682]:
df_2016_merged.columns
Out[682]:
Index(['GEOID', 'Average of ELEP', 'Average of GASP', 'Average of FULP',
       'Total_Energy_Cost', 'GEOID_TRACT_10', 'OID_TRACT_20', 'GEOID_TRACT_20',
       'NAMELSAD_TRACT_20', 'AREALAND_TRACT_20', 'AREAWATER_TRACT_20',
       'MTFCC_TRACT_20', 'FUNCSTAT_TRACT_20', 'OID_TRACT_10',
       'NAMELSAD_TRACT_10', 'AREALAND_TRACT_10', 'AREAWATER_TRACT_10',
       'MTFCC_TRACT_10', 'FUNCSTAT_TRACT_10', 'AREALAND_PART',
       'AREAWATER_PART'],
      dtype='object')
In [683]:
df_2016_converted = df_2016_merged[['GEOID_TRACT_20','Total_Energy_Cost']]
In [684]:
df_2016_converted['GEOID'] = df_2016_converted['GEOID_TRACT_20'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\616540618.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_2016_converted['GEOID'] = df_2016_converted['GEOID_TRACT_20'].astype(str)
In [685]:
df_2016_converted.columns
Out[685]:
Index(['GEOID_TRACT_20', 'Total_Energy_Cost', 'GEOID'], dtype='object')
In [686]:
df_2016_converted.head()
Out[686]:
GEOID_TRACT_20 Total_Energy_Cost GEOID
0 51001090101 4783.176599 51001090101
1 51001090102 4783.176599 51001090102
2 51001090201 3712.642528 51001090201
3 51001090202 3712.642528 51001090202
4 51001090300 3076.827772 51001090300
In [ ]:
In [687]:
## Obtaining Income to calculate Energy Burden
In [688]:
## Census Tracts
## Obtaining data for 8 localities - Ashland will be created separately from Hanover and then removed from Hanover to prevent overlap

# ACS demographic data (selected variables)
host = 'https://api.census.gov/data'
year = f"/{Year}"
dataset = '/acs/acs5/profile'
get = '?get='
variables = 'DP03_0062E'
for_param = '&for=tract:*'#All tracts
in_param = '&in=state:'
state = '51'
in_param2 = '&in=county:'
# for counties in the PlanRVA footprint
# Charles City [036], Chesterfield [041], Goochland [075], Hanover [085], Henrico [087], New Kent [127], Powhatan [145], Richmond [760]
county = '036,041,075,085,087,127,145,760,095'
# for counties in the Richmond MSA
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#county = '007,033,036,041,570,049,053,595,075,081,085,087,670,101,109,127,730,145,149,760,181,183'
#insert usr_key
query_url_HC_YM_acs2021 = f"{host}{year}{dataset}{get}{variables}{for_param}{in_param}{state}{in_param2}{county}{usr_key}"
response_HC_YM_acs2021 = requests.get(query_url_HC_YM_acs2021)
In [689]:
query_url_HC_YM_acs2021
Out[689]:
'https://api.census.gov/data/2022/acs/acs5/profile?get=DP03_0062E&for=tract:*&in=state:51&in=county:036,041,075,085,087,127,145,760,095&key=6b0a4e1479613a5be20d0e11af6d4711d87d2a61'
In [690]:
response_HC_YM_acs2021
Out[690]:
<Response [200]>
In [691]:
#Convert json output to DataFrame and Using first row as columns then dropping that row (first)
data_2021 = response_HC_YM_acs2021.json()
df_acs2021 = pd.DataFrame(data_2021, columns= data_2021[0])
df_acs2021 = df_acs2021.drop(df_acs2021.index[0])
df_acs2021.head()
Out[691]:
DP03_0062E state county tract
1 75446 51 036 600100
2 64130 51 036 600200
3 62738 51 036 600300
4 130057 51 041 100106
5 39783 51 041 100107
In [692]:
#Getting Ashland Data-
In [693]:
df_acs2021_a=df_acs2021[(df_acs2021['county']=='085')&((df_acs2021['tract']=='320601')|(df_acs2021['tract']=='320602'))].replace('085','03368')
In [694]:
df_acs2021_a # Ashland Census Tracts
Out[694]:
DP03_0062E state county tract
90 64188 51 03368 320601
91 64271 51 03368 320602
In [695]:
# Combine both DFs (8 localities and Ashland) using concat
frames = [df_acs2021,df_acs2021_a]
df_acs2021_comb = pd.DataFrame()
df_acs2021_comb = pd.concat(frames) #DataFrame Concatanation
df_acs2021_comb.head()
Out[695]:
DP03_0062E state county tract
1 75446 51 036 600100
2 64130 51 036 600200
3 62738 51 036 600300
4 130057 51 041 100106
5 39783 51 041 100107
In [696]:
# create a new column for setting Index by defining new function and then applying it on df -
def index_df (row):
    if row['county'] == '03368':
        return '0'
    elif row['county'] == '036':
        return '1'
    elif row['county'] == '075':
        return '3'
    elif row['county'] == '087':
        return '5'
    elif row['county'] == '127':
        return '6'
    elif row['county'] == '145':
        return '7'
    elif row['county'] == '041':
        return '2'
    elif row['county'] == '085':
        return '4'
    elif row['county'] == '760':
        return '8'
    elif row['county'] == '095':
        return '9'
    else:
        return 'unknown'
In [697]:
#Applying created function on df
df_acs2021_comb['index_df'] = df_acs2021_comb.apply(lambda row: index_df(row), axis = 1)
In [698]:
df_acs2021_comb.index = df_acs2021_comb['index_df'] #Setting Index
In [699]:
df_acs2021_comb = df_acs2021_comb.sort_index().reset_index(drop=True) # Sorting by index and resetting
In [700]:
df_acs2021_comb=df_acs2021_comb.drop(['index_df'],axis=1)  #Drop created index columns
In [ ]:
In [701]:
## Need to remove Ashland tracts from Hanover so that they dont overlap in shp

index_names = df_acs2021_comb[(df_acs2021_comb['county']=='085')&((df_acs2021_comb['tract']=='320601')|(df_acs2021_comb['tract']=='320602'))].index

index_names
Out[701]:
Int64Index([105, 110], dtype='int64')
In [702]:
df_acs2021_comb.drop(index_names, inplace = True)
In [ ]:
In [ ]:
In [703]:
df_acs2021 = df_acs2021_comb #A Copy of df if needed before Editing further
In [704]:
# Create GEOID
df_acs2021 ['GEOID'] = df_acs2021['state']+df_acs2021['county']+df_acs2021['tract']
In [705]:
## Giving Ashland tracts the correct GEOID for merge later with shp
df_acs2021 = df_acs2021.replace({'5103368320601':'51085320601','5103368320602':'51085320602'})
In [706]:
df_acs2021['Year'] = Year
df_acs2021.head()
Out[706]:
DP03_0062E state county tract GEOID Year
0 64271 51 03368 320602 51085320602 2022
1 64188 51 03368 320601 51085320601 2022
2 75446 51 036 600100 51036600100 2022
3 64130 51 036 600200 51036600200 2022
4 62738 51 036 600300 51036600300 2022
In [707]:
df_acs2021['DP03_0062E'] = df_acs2021['DP03_0062E'].astype(int)


df_acs2021=df_acs2021.replace(-666666666,0)
df_acs2021.head()
Out[707]:
DP03_0062E state county tract GEOID Year
0 64271 51 03368 320602 51085320602 2022
1 64188 51 03368 320601 51085320601 2022
2 75446 51 036 600100 51036600100 2022
3 64130 51 036 600200 51036600200 2022
4 62738 51 036 600300 51036600300 2022
In [708]:
# create a new column for short locality name by defining new function and then applying it on df -
def locality (row):
    if row['county'] == '03368':
        return 'Ashland'
    elif row['county'] == '036':
        return 'Charles City'
    elif row['county'] == '075':
        return 'Goochland'
    elif row['county'] == '087':
        return 'Henrico'
    elif row['county'] == '127':
        return 'New Kent'
    elif row['county'] == '145':
        return 'Powhatan'
    elif row['county'] == '041':
        return 'Chesterfield'
    elif row['county'] == '085':
        return 'Hanover'
    elif row['county'] == '760':
        return 'Richmond'
    elif row['county'] == '095':
        return 'James City'
    else:
        return 'unknown'
In [709]:
df_acs2021['locality'] = df_acs2021.apply(lambda row: locality(row), axis = 1)
In [710]:
df_acs2021.columns
Out[710]:
Index(['DP03_0062E', 'state', 'county', 'tract', 'GEOID', 'Year', 'locality'], dtype='object')
In [711]:
# Set of first five characters to filter by
geoid_set = {'51036', '51041', '51075', '51085', '51087', '51127', '51145', '51760', '51095'}

# Extract the first five characters of the GEOID column and filter
df_filtered = df_2016_converted[df_2016_converted['GEOID'].str[:5].isin(geoid_set)]
In [712]:
df_filtered.head()
Out[712]:
GEOID_TRACT_20 Total_Energy_Cost GEOID
390 51036600100 2851.694484 51036600100
391 51036600200 3064.943912 51036600200
392 51036600300 3318.349738 51036600300
397 51041100106 2870.621073 51041100106
398 51760070400 2870.621073 51760070400
In [713]:
df_filtered['GEOID'] = df_filtered['GEOID'].astype(str)
df_acs2021['GEOID'] = df_acs2021['GEOID'].astype(str)

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\1957513825.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_filtered['GEOID'] = df_filtered['GEOID'].astype(str)
In [714]:
df_energy2 = pd.merge(df_acs2021,df_filtered,on = 'GEOID', how = 'outer')
In [715]:
df_energy2.columns
Out[715]:
Index(['DP03_0062E', 'state', 'county', 'tract', 'GEOID', 'Year', 'locality',
       'GEOID_TRACT_20', 'Total_Energy_Cost'],
      dtype='object')
In [716]:
df_energy2['Total_Energy_Cost'].unique()[:5]
Out[716]:
array([3177.33144273, 2325.7739088 , 2863.41085132, 2420.66279677,
       2851.69448372])
In [717]:
# Ensure the columns are numeric and handle NaN values
df_energy2['Total_Energy_Cost'] = pd.to_numeric(df_energy2['Total_Energy_Cost'].fillna(0))
df_energy2['DP03_0062E'] = pd.to_numeric(df_energy2['DP03_0062E'].fillna(0))

# Replace all 0s in DP03_0062E with 1s to avoid division by zero
df_energy2['DP03_0062E'] = df_energy2['DP03_0062E'].replace(0, 1)

# Calculate 'En_Burden'
df_energy2['En_Burden'] = df_energy2['Total_Energy_Cost'] / df_energy2['DP03_0062E'] * 100

# Identify the outliers (example threshold for demonstration)
outlier_threshold = 10000
outliers = df_energy2['En_Burden'] > outlier_threshold

# Replace outliers with the median of the 'En_Burden' column
median_en_burden = df_energy2.loc[~outliers, 'En_Burden'].median()
df_energy2.loc[outliers, 'En_Burden'] = median_en_burden

# Categorize En_Burden with numerical values
def categorize_energy_burden(value):
    if value < 2:
        return 0  # No Burden
    elif 2 <= value < 6:
        return 1  # Some Burden
    else:
        return 2  # High Burden

df_energy2['En_Burden'] = df_energy2['En_Burden'].apply(categorize_energy_burden)

# Display the DataFrame after handling outliers
#print(df_energy2[['GEOID', 'Total_Energy_Cost', 'DP03_0062E', 'En_Burden']])

# Sort the DataFrame by 'En_Burden' in descending order
df_sorted = df_energy2.sort_values(by='En_Burden', ascending=False)

# Display the top 20 values
top_20_en_burden = df_sorted.head(20)

# Display the result
print(top_20_en_burden[['GEOID', 'En_Burden']])

           GEOID  En_Burden
409  51760020100          2
397  51760010300          2
390  51760020400          2
418  51760030100          2
417  51760030100          2
381  51760060800          2
375  51760040300          2
408  51760020100          2
410  51760020200          2
272  51087200126          1
281  51087200303          1
280  51087200305          1
279  51087200404          1
277  51087200409          1
275  51087200407          1
273  51087200140          1
0    51085320602          1
271  51087200413          1
252  51087200136          1
270  51087200106          1
In [718]:
df_energy2['En_Burden'].max()
Out[718]:
2
In [719]:
gdf_asthma = pd.merge(gdf_US_CT,df_energy2, on = 'GEOID', how = 'outer')
In [720]:
gdf_asthma.head()
Out[720]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER ... geometry DP03_0062E state county tract Year locality GEOID_TRACT_20 Total_Energy_Cost En_Burden
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 ... POLYGON ((-77.74029 37.87408, -77.73998 37.874... 93117.0 51 085 320100 2022 Hanover 51085320100 2998.407418 1.0
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... POLYGON ((-77.36115 37.60754, -77.36084 37.607... 89083.0 51 085 321201 2022 Hanover 51085321201 2240.047877 1.0
2 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 ... POLYGON ((-77.36115 37.60754, -77.36084 37.607... 89083.0 51 085 321201 2022 Hanover 51085321201 2276.887172 1.0
3 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... POLYGON ((-77.38286 37.59526, -77.38280 37.595... 60197.0 51 085 321202 2022 Hanover 51085321202 2314.782910 1.0
4 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 ... POLYGON ((-77.38286 37.59526, -77.38280 37.595... 60197.0 51 085 321202 2022 Hanover 51085321202 1934.532106 1.0

5 rows × 22 columns

In [721]:
# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Filter the dataframe
gdf_asthma_subset = gdf_asthma[gdf_asthma['COUNTYFP'].isin(countyfp_values)]
In [722]:
fig, ax = plt.subplots(figsize = (20,16)) 

gdf_asthma_subset.plot(column='En_Burden',cmap = 'YlOrRd',ax=ax,legend = 'True') # Values 
gdf_County_RVA.plot(color='None',edgecolor='k',linewidth = 0.5,ax=ax) ## Locality boundaries

# Add Title and Labels
ax.set_title('Energy Burden by Census Tract', fontsize=20)
    
# Add labels for counties
for idx, row in gdf_County_RVA.iterrows():
    ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                horizontalalignment='center', fontsize=12, color='black')

# Add North Arrow
x, y, arrow_length = 0.1, 0.95, 0.1
ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
# Add text box
textstr = "Energy Burden: % Share of overall income spent on Energy: Electricity, Gas, Fuel"
props = dict(boxstyle='round,pad=0.5', facecolor='white', edgecolor='black')
ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=14,
        verticalalignment='top', horizontalalignment='right', bbox=props)

# Save the map
map_name = 'Energy_Burden.png'  # Change the map name as needed
save_path = os.path.join(save_dir, map_name)
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
In [ ]:
In [ ]:
In [723]:
gdf_energy = gdf_asthma_subset
In [724]:
df_acs2021_var23 = gdf_energy
In [725]:
len(gdf_energy)
Out[725]:
462
In [726]:
dfs = [
    df_acs2021_var1, df_acs2021_var2, df_acs2021_var3, df_acs2021_var4, 
    df_acs2021_var5, df_acs2021_var6, df_acs2021_var7, df_acs2021_var8, 
    df_acs2021_var9, df_acs2021_var10, df_acs2021_var11,
    df_acs2021_var12, df_acs2021_var13, df_acs2021_var14, df_acs2021_var15,
    df_acs2021_var16, df_acs2021_var17, df_acs2021_var18, df_acs2021_var19,
    df_acs2021_var20, df_acs2021_var21, df_acs2021_var22, df_acs2021_var23
]
In [727]:
for i, df in enumerate(dfs, start=1):
    print(f'Length of df_acs2021_var{i}: {len(df)}')

Length of df_acs2021_var1: 296
Length of df_acs2021_var2: 296
Length of df_acs2021_var3: 296
Length of df_acs2021_var4: 296
Length of df_acs2021_var5: 296
Length of df_acs2021_var6: 296
Length of df_acs2021_var7: 296
Length of df_acs2021_var8: 296
Length of df_acs2021_var9: 296
Length of df_acs2021_var10: 296
Length of df_acs2021_var11: 296
Length of df_acs2021_var12: 296
Length of df_acs2021_var13: 290
Length of df_acs2021_var14: 462
Length of df_acs2021_var15: 462
Length of df_acs2021_var16: 462
Length of df_acs2021_var17: 462
Length of df_acs2021_var18: 462
Length of df_acs2021_var19: 462
Length of df_acs2021_var20: 462
Length of df_acs2021_var21: 462
Length of df_acs2021_var22: 462
Length of df_acs2021_var23: 462
In [728]:
# List of DataFrames
dfs = [
    df_acs2021_var14, df_acs2021_var15, df_acs2021_var16, df_acs2021_var17, 
    df_acs2021_var18, df_acs2021_var19, df_acs2021_var20, df_acs2021_var21, 
    df_acs2021_var22, df_acs2021_var23
]

# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Create a new list to store the filtered DataFrames
filtered_dfs = []

# Filter each DataFrame based on COUNTYFP values
for df in dfs:
    filtered_df = df[df['COUNTYFP'].isin(countyfp_values)]
    filtered_dfs.append(filtered_df)

# Unpack the filtered DataFrames back into individual variables
df_acs2021_var14, df_acs2021_var15, df_acs2021_var16, df_acs2021_var17, \
df_acs2021_var18, df_acs2021_var19, df_acs2021_var20, df_acs2021_var21, \
df_acs2021_var22, df_acs2021_var23 = filtered_dfs

# Display the result to check
for i, df in enumerate(filtered_dfs, start=14):
    print(f"Length of df_acs2021_var{i}: {len(df)}")

Length of df_acs2021_var14: 462
Length of df_acs2021_var15: 462
Length of df_acs2021_var16: 462
Length of df_acs2021_var17: 462
Length of df_acs2021_var18: 462
Length of df_acs2021_var19: 462
Length of df_acs2021_var20: 462
Length of df_acs2021_var21: 462
Length of df_acs2021_var22: 462
Length of df_acs2021_var23: 462
In [729]:
df_acs2021_var14.columns
Out[729]:
Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
In [730]:
# List of DataFrames
dfs = [
    df_acs2021_var14, df_acs2021_var15, df_acs2021_var16, df_acs2021_var17, 
    df_acs2021_var18, df_acs2021_var19, df_acs2021_var20, df_acs2021_var21, 
    df_acs2021_var22, df_acs2021_var23
]

# List of desired COUNTYFP values
countyfp_values = ['036', '041', '075', '085', '087', '127', '145', '760', '095']

# Create a new list to store the filtered DataFrames
filtered_dfs = []

# Filter each DataFrame based on COUNTYFP values
for df in dfs:
    filtered_df = df[df['COUNTYFP'].isin(countyfp_values)]
    filtered_dfs.append(filtered_df)

# Unpack the filtered DataFrames back into individual variables
df_acs2021_var14, df_acs2021_var15, df_acs2021_var16, df_acs2021_var17, \
df_acs2021_var18, df_acs2021_var19, df_acs2021_var20, df_acs2021_var21, \
df_acs2021_var22, df_acs2021_var23 = filtered_dfs

# Display the result to check
for i, df in enumerate(filtered_dfs, start=14):
    print(f"df_acs2021_var{i}['COUNTYFP'].unique(): {df['COUNTYFP'].unique()}")

df_acs2021_var14['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var15['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var16['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var17['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var18['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var19['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var20['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var21['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var22['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
df_acs2021_var23['COUNTYFP'].unique(): ['085' '087' '760' '041' '145' '036' '095' '075' '127']
In [731]:
for i, df in enumerate(dfs, start=1):
    print(f'Length of df_acs2021_var{i}: {df.columns}')

Length of df_acs2021_var1: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var2: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var3: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var4: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var5: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var6: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var7: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var8: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var9: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'GEOID_TRACT_20', 'Data_Value'],
      dtype='object')
Length of df_acs2021_var10: Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'DP03_0062E', 'state', 'county', 'tract', 'Year', 'locality',
       'GEOID_TRACT_20', 'Total_Energy_Cost', 'En_Burden'],
      dtype='object')

Next Works:¶

1. Get the final mapping column names for all variables and a list of all variables together.¶

2. Check for High_Confidence_Limit meaning and write down.¶

3. Correct title wording in maps for clarification and finalize.¶

4. Finalize next steps for Index creation after finalizing all the values being used.¶

5. Check old Census Tracts files if PLACES Data is created for those instead.¶

In [732]:
# # List of DataFrames for each category
# economic_dfs = [
#     df_acs2021_var1, df_acs2021_var2, df_acs2021_var3, df_acs2021_var4, 
#     df_acs2021_var10, df_acs2021_var13, df_acs2021_var23
# ]

# social_cultural_dfs = [
#     df_acs2021_var6, df_acs2021_var7, df_acs2021_var8, df_acs2021_var9, df_acs2021_var11
# ]

# health_dfs = [
#     df_acs2021_var5, df_acs2021_var12, df_acs2021_var14, df_acs2021_var15, df_acs2021_var16, 
#     df_acs2021_var17, df_acs2021_var18, df_acs2021_var19, df_acs2021_var20, 
#     df_acs2021_var21, df_acs2021_var22
# ]

# # Define columns to keep for each DataFrame
# columns_to_keep = {
#     'economic': ['GEOID', 'Perc_pov', 'Perc_BurdRU', 'Perc_BurdHousU', 'Perc_NoVeh', 'En_Burden', 'HL_rating', 'Perc_Crowd'],
#     'social_cultural': ['GEOID', 'Perc_NoDis', 'Perc_Over65', 'Perc_Under5', 'Perc_NoEng', 'Perc_UnS'],
#     'health': ['GEOID','Perc_NoIns','Perc_Preg12', 'Data_Value']
# }
In [733]:
# List of DataFrames for each category
economic_dfs = [
    df_acs2021_var1[['GEOID', 'Perc_pov']], df_acs2021_var2[['GEOID', 'Perc_BurdRU']], 
    df_acs2021_var3[['GEOID', 'Perc_BurdHousU']], df_acs2021_var4[['GEOID', 'Perc_NoVeh']], 
    df_acs2021_var10[['GEOID', 'Perc_Crowd']], df_acs2021_var13[['GEOID', 'HL_rating']], 
    df_acs2021_var23[['GEOID', 'En_Burden']]
]

social_cultural_dfs = [
    df_acs2021_var6[['GEOID', 'Perc_NoDis']], df_acs2021_var7[['GEOID', 'Perc_Over65']], 
    df_acs2021_var8[['GEOID', 'Perc_Under5']], df_acs2021_var9[['GEOID', 'Perc_NoEng']], 
    df_acs2021_var11[['GEOID', 'Perc_UnS']]
]

health_dfs = [
    df_acs2021_var5[['GEOID', 'Perc_NoIns']], df_acs2021_var12[['GEOID', 'Perc_Preg12']], 
    df_acs2021_var14[['GEOID', 'Data_Value']], 
    df_acs2021_var15[['GEOID', 'Data_Value']], df_acs2021_var16[['GEOID', 'Data_Value']], 
    df_acs2021_var17[['GEOID', 'Data_Value']], df_acs2021_var18[['GEOID', 'Data_Value']], 
    df_acs2021_var19[['GEOID', 'Data_Value']], df_acs2021_var20[['GEOID', 'Data_Value']], 
    df_acs2021_var21[['GEOID', 'Data_Value']], df_acs2021_var22[['GEOID', 'Data_Value']]
]

# Define columns to keep for each DataFrame
columns_to_keep = {
    'economic': ['GEOID', 'Perc_pov', 'Perc_BurdRU', 'Perc_BurdHousU', 'Perc_NoVeh', 'En_Burden', 'HL_rating', 'Perc_Crowd'],
    'social_cultural': ['GEOID', 'Perc_NoDis', 'Perc_Over65', 'Perc_Under5', 'Perc_NoEng', 'Perc_UnS'],
    'health': ['GEOID','Perc_NoIns','Perc_Preg12', 'Data_Value']
}
In [734]:
from sklearn.preprocessing import MinMaxScaler

def calculate_sub_index(dfs, columns, index_name):
    # Ensure each DataFrame only contains the columns to keep
    dfs = [df[[col for col in columns if col in df.columns]] for df in dfs]
    
    # Merge DataFrames
    df_final = dfs[0]
    for df in dfs[1:]:
        df_final = pd.merge(df_final, df, on='GEOID', how='outer', suffixes=('', '_duplicate'))
        df_final = df_final.loc[:,~df_final.columns.str.endswith('_duplicate')]
    
    # Normalize columns
    scaler = MinMaxScaler()
    df_final[columns[1:]] = scaler.fit_transform(df_final[columns[1:]])
    
    # Calculate sub-index
    df_final[index_name] = df_final[columns[1:]].mean(axis=1)
    
    return df_final[['GEOID', index_name]]

# Calculate sub-indices
economic_df = calculate_sub_index(economic_dfs, columns_to_keep['economic'], 'Economic_Index')
social_cultural_df = calculate_sub_index(social_cultural_dfs, columns_to_keep['social_cultural'], 'Social_Cultural_Index')
health_df = calculate_sub_index(health_dfs, columns_to_keep['health'], 'Health_Index')
In [735]:
# Merge sub-indices
final_df = pd.merge(economic_df, social_cultural_df, on='GEOID', how='outer')
final_df = pd.merge(final_df, health_df, on='GEOID', how='outer')
In [736]:
final_df.head()
Out[736]:
GEOID Economic_Index Social_Cultural_Index Health_Index
0 51085320602 0.171382 0.294059 0.258964
1 51085320602 0.171382 0.294059 0.258964
2 51085320602 0.171382 0.294059 0.258964
3 51085320602 0.171382 0.294059 0.258964
4 51085320602 0.171382 0.294059 0.258964
In [769]:
from sklearn.preprocessing import MinMaxScaler
import pandas as pd

# Assuming final_df is your DataFrame and contains the relevant sub-indices
# Normalize sub-indices
scaler = MinMaxScaler()
final_df[['Economic_Index', 'Social_Cultural_Index', 'Health_Index']] = scaler.fit_transform(final_df[['Economic_Index', 'Social_Cultural_Index', 'Health_Index']])

# Calculate final heat sensitivity index
final_df['Heat_Sensitivity_Index'] = final_df[['Economic_Index', 'Social_Cultural_Index', 'Health_Index']].mean(axis=1)

# Normalize the final heat sensitivity index
final_df['Heat_Sensitivity_Index'] = scaler.fit_transform(final_df[['Heat_Sensitivity_Index']])

# Display the head of the final DataFrame
print(final_df.head())

         GEOID  Economic_Index  Social_Cultural_Index  Health_Index  \
0  51085320602        0.309182               0.452187      0.373786   
1  51085320602        0.309182               0.452187      0.373786   
2  51085320602        0.309182               0.452187      0.373786   
3  51085320602        0.309182               0.452187      0.373786   
4  51085320602        0.309182               0.452187      0.373786   

   Heat_Sensitivity_Index  
0                0.430111  
1                0.430111  
2                0.430111  
3                0.430111  
4                0.430111
In [770]:
import pandas as pd

# Assuming gdf_final is your dataframe
# Group by 'GEOID' and take the mean of each group
final_df_unique = final_df.groupby('GEOID').mean().reset_index()

# Display the first few rows of the new dataframe
print(final_df_unique.head())

         GEOID  Economic_Index  Social_Cultural_Index  Health_Index  \
0  51036600100        0.322675               0.462966      0.317886   
1  51036600200        0.271747               0.563924      0.316531   
2  51036600300        0.316296               0.583518      0.199188   
3  51041100106        0.110661               0.186568      0.212030   
4  51041100107        0.544101               0.646007      0.505472   

   Heat_Sensitivity_Index  
0                0.418128  
1                0.436570  
2                0.416413  
3                0.192959  
4                0.642457
In [771]:
import matplotlib.pyplot as plt

# List of indices to plot
indices = ['Economic_Index', 'Social_Cultural_Index', 'Health_Index', 'Heat_Sensitivity_Index']

# Plotting histograms for each index
for index in indices:
    plt.figure(figsize=(10, 6))
    plt.hist(final_df_unique[index], bins=30, edgecolor='black')
    plt.title(f'Histogram of {index}')
    plt.xlabel(index)
    plt.ylabel('Frequency')
    plt.show()
In [ ]:
In [772]:
import geopandas as gpd

# Merge final DataFrame with GeoDataFrame for shapefile output
gdf = gdf_US_CT  # Replace with the actual path to your shapefile
gdf_final = gdf.merge(final_df_unique, on='GEOID', how = 'inner')
In [773]:
import os
os.getcwd()
Out[773]:
'C:\\Users\\rsingh\\OneDrive - PlanRVA\\Documents\\2_Projects\\Environmental_Analysis\\Heat Sensitivity Index\\Plots\\Final Files'
In [774]:
os.chdir(r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\2_Projects\Environmental_Analysis\Heat Sensitivity Index\Plots\Final Files")
In [823]:
# Save to Excel
final_df_unique.to_excel('Heat_Sensitivity_Index.xlsx', index=False)

# Save to Shapefile
gdf_final.to_file('Heat_Sensitivity_Index.shp')

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\1534631051.py:5: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.
  gdf_final.to_file('Heat_Sensitivity_Index.shp')
In [776]:
gdf_final.head()
Out[776]:
STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON geometry Economic_Index Social_Cultural_Index Health_Index Heat_Sensitivity_Index
0 51 085 320100 51085320100 3201 Census Tract 3201 G5020 S 328537534 2927606 +37.8856871 -077.5726776 POLYGON ((-77.74029 37.87408, -77.73998 37.874... 0.238456 0.351171 0.162407 0.284946
1 51 085 321201 51085321201 3212.01 Census Tract 3212.01 G5020 S 11255521 16144 +37.5867060 -077.3295398 POLYGON ((-77.36115 37.60754, -77.36084 37.607... 0.263398 0.275310 0.157531 0.263806
2 51 085 321202 51085321202 3212.02 Census Tract 3212.02 G5020 S 8887075 96758 +37.5858032 -077.3462287 POLYGON ((-77.38286 37.59526, -77.38280 37.595... 0.442252 0.288278 0.228570 0.363404
3 51 085 320400 51085320400 3204 Census Tract 3204 G5020 S 58245255 348336 +37.7231726 -077.5209363 POLYGON ((-77.61233 37.75986, -77.61216 37.760... 0.310036 0.290657 0.341903 0.357150
4 51 087 201002 51087201002 2010.02 Census Tract 2010.02 G5020 S 3022230 4272 +37.5725532 -077.4043263 POLYGON ((-77.41507 37.56327, -77.41492 37.563... 0.448440 0.508998 0.348747 0.494915
In [777]:
gdf_final.columns
Out[777]:
Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'GEOID', 'NAME', 'NAMELSAD', 'MTFCC',
       'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry',
       'Economic_Index', 'Social_Cultural_Index', 'Health_Index',
       'Heat_Sensitivity_Index'],
      dtype='object')
In [778]:
import matplotlib.pyplot as plt
import os
import geopandas as gpd

# Function to plot a map
def plot_map(gdf, column, title, map_name, gdf_boundaries, save_dir='.'):
    # Check and align CRS
    if gdf.crs != gdf_boundaries.crs:
        gdf_boundaries = gdf_boundaries.to_crs(gdf.crs)
    
    # Create the plot
    fig, ax = plt.subplots(figsize=(20, 16))
    
    # Plot the choropleth map
    gdf.plot(column=column, cmap='YlOrRd', ax=ax, legend=True)
    
    # Plot the county boundaries
    gdf_boundaries.plot(color='none', edgecolor='k', linewidth=0.5, ax=ax)  # Ensure 'color' is 'none' to make the counties transparent
    
    # Add Title and Labels
    ax.set_title(title, fontsize=20)
    
    # Add labels for counties
    for idx, row in gdf_boundaries.iterrows():
        ax.annotate(row['NAME'], xy=(row.geometry.centroid.x, row.geometry.centroid.y),
                    horizontalalignment='center', fontsize=12, color='black')
    
    # Add North Arrow
    x, y, arrow_length = 0.1, 0.95, 0.1
    ax.annotate('N', xy=(x, y), xytext=(x, y - arrow_length),
                arrowprops=dict(facecolor='black', width=5, headwidth=15),
                ha='center', va='center', fontsize=20,
                xycoords=ax.transAxes)
    
    # Save the map
    save_path = os.path.join(save_dir, map_name)
    plt.savefig(save_path, dpi=300, bbox_inches='tight')
    plt.show()

# Plot the maps for each index
plot_map(gdf_final, 'Economic_Index', 'Economic Vulnerability Index by Census Tract', 'Economic_SVI.png', gdf_County_RVA)
plot_map(gdf_final, 'Social_Cultural_Index', 'Social/Cultural Vulnerability Index by Census Tract', 'Social_Cultural_Index.png', gdf_County_RVA)
plot_map(gdf_final, 'Health_Index', 'Health Vulnerability Index by Census Tract', 'Health_Index.png', gdf_County_RVA)
plot_map(gdf_final, 'Heat_Sensitivity_Index', 'Heat Sensitivity Vulnerability Index by Census Tract', 'Heat_Sensitivity_Index.png', gdf_County_RVA)
In [ ]:
In [801]:
## Combining DFs to create comprehensive original variables df 'df_combined'
In [802]:
import pandas as pd

# Define a function to combine a list of DataFrames on 'GEOID'
def combine_dfs(dfs):
    combined_df = dfs[0]
    for df in dfs[1:]:
        combined_df = pd.merge(combined_df, df, on='GEOID', how='outer', suffixes=('', '_duplicate'))
    return combined_df

# Combine Economic DataFrames
economic_combined = combine_dfs(economic_dfs)

# Combine Social/Cultural DataFrames
social_cultural_combined = combine_dfs(social_cultural_dfs)

# Combine Health DataFrames
health_combined = combine_dfs(health_dfs)

# Display the combined DataFrames
print(economic_combined.head())
print(social_cultural_combined.head())
print(health_combined.head())

         GEOID   Perc_pov  Perc_BurdRU  Perc_BurdHousU  Perc_NoVeh  \
0  51085320602   8.841463     7.612457       13.425606    1.245675   
1  51085320602   8.841463     7.612457       13.425606    1.245675   
2  51085320602   8.841463     7.612457       13.425606    1.245675   
3  51085320601  19.146504    27.843685        5.792045    7.117934   
4  51085320601  19.146504    27.843685        5.792045    7.117934   

   Perc_Crowd  HL_rating  En_Burden  
0    0.276817        2.0        1.0  
1    0.276817        2.0        1.0  
2    0.276817        2.0        1.0  
3    4.396371        2.0        1.0  
4    4.396371        2.0        1.0  
         GEOID  Perc_NoDis  Perc_Over65  Perc_Under5  Perc_NoEng   Perc_UnS
0  51085320602   11.382114    24.457995     5.657182    5.826558  36.212737
1  51085320601   10.382930    11.830825     4.210326    3.105353  30.843970
2  51036600100   16.642175    22.520206     5.216752    0.771492  43.681117
3  51036600200   19.039596    27.169334     3.201348    0.168492  60.067397
4  51036600300   23.377404    28.425481     2.584135    0.000000  53.966346
         GEOID  Perc_NoIns  Perc_Preg12  Data_Value  Data_Value_duplicate  \
0  51085320602   14.193767     1.422764         8.8                  10.8   
1  51085320602   14.193767     1.422764         8.8                  10.8   
2  51085320602   14.193767     1.422764         8.8                  10.8   
3  51085320602   14.193767     1.422764         8.8                  10.8   
4  51085320602   14.193767     1.422764         8.8                  10.8   

   Data_Value_duplicate  Data_Value_duplicate  Data_Value_duplicate  \
0                  36.1                   6.8                  36.0   
1                  36.1                   6.8                  36.0   
2                  36.1                   6.8                  36.0   
3                  36.1                   6.8                  36.0   
4                  36.1                   6.8                  36.0   

   Data_Value_duplicate  Data_Value_duplicate  Data_Value_duplicate  \
0                   6.9                   3.0                  13.5   
1                   6.9                   3.0                  13.5   
2                   6.9                   3.0                  13.5   
3                   6.9                   3.0                  11.6   
4                   6.9                   3.0                  11.6   

   Data_Value_duplicate  
0                   6.5  
1                   5.1  
2                   6.4  
3                   6.5  
4                   5.1
In [803]:
len(economic_combined)
Out[803]:
473
In [804]:
len(social_cultural_combined)
Out[804]:
296
In [805]:
len(health_combined)
Out[805]:
13747122
In [806]:
import pandas as pd

# Assuming gdf_final is your dataframe
# Group by 'GEOID' and take the mean of each group
health_combined = health_combined.groupby('GEOID').mean().reset_index()

# Display the first few rows of the new dataframe
print(health_combined.head())

         GEOID  Perc_NoIns  Perc_Preg12  Data_Value  Data_Value_duplicate  \
0  51036600100    7.090375     0.771492   11.100000                16.000   
1  51036600200    6.444819     0.758214   11.300000                17.200   
2  51036600300    4.867788     0.000000   10.800000                16.200   
3  51041100106    8.699039     0.389509    9.133333                10.600   
4  51041100107   12.106730     1.991239   11.100000                11.525   

   Data_Value_duplicate  Data_Value_duplicate  Data_Value_duplicate  \
0             44.000000                  6.70                41.800   
1             45.100000                  6.70                42.400   
2             45.300000                  7.00                40.800   
3             36.033333                  7.90                30.600   
4             35.250000                  4.85                39.075   

   Data_Value_duplicate  Data_Value_duplicate  Data_Value_duplicate  \
0              7.500000              4.500000             15.000000   
1              7.500000              4.800000             14.800000   
2              7.300000              4.500000             13.700000   
3              6.133333              2.866667             11.566667   
4              5.050000              3.125000             15.525000   

   Data_Value_duplicate  
0              8.700000  
1              8.200000  
2              8.000000  
3              5.566667  
4              5.925000
In [807]:
# Merge all combined DataFrames into one final DataFrame
df_combined = pd.merge(economic_combined, social_cultural_combined, on='GEOID', how='outer')
df_combined = pd.merge(df_combined, health_combined, on='GEOID', how='outer')

# Display the final combined DataFrame
print(df_combined.head())

         GEOID   Perc_pov  Perc_BurdRU  Perc_BurdHousU  Perc_NoVeh  \
0  51085320602   8.841463     7.612457       13.425606    1.245675   
1  51085320602   8.841463     7.612457       13.425606    1.245675   
2  51085320602   8.841463     7.612457       13.425606    1.245675   
3  51085320601  19.146504    27.843685        5.792045    7.117934   
4  51085320601  19.146504    27.843685        5.792045    7.117934   

   Perc_Crowd  HL_rating  En_Burden  Perc_NoDis  Perc_Over65  ...  \
0    0.276817        2.0        1.0   11.382114    24.457995  ...   
1    0.276817        2.0        1.0   11.382114    24.457995  ...   
2    0.276817        2.0        1.0   11.382114    24.457995  ...   
3    4.396371        2.0        1.0   10.382930    11.830825  ...   
4    4.396371        2.0        1.0   10.382930    11.830825  ...   

   Perc_Preg12  Data_Value  Data_Value_duplicate  Data_Value_duplicate  \
0     1.422764    9.033333                  10.5                 36.30   
1     1.422764    9.033333                  10.5                 36.30   
2     1.422764    9.033333                  10.5                 36.30   
3     1.390741   10.000000                  10.2                 33.65   
4     1.390741   10.000000                  10.2                 33.65   

   Data_Value_duplicate  Data_Value_duplicate  Data_Value_duplicate  \
0              7.366667             36.733333              6.033333   
1              7.366667             36.733333              6.033333   
2              7.366667             36.733333              6.033333   
3              6.100000             36.900000              5.600000   
4              6.100000             36.900000              5.600000   

   Data_Value_duplicate  Data_Value_duplicate  Data_Value_duplicate  
0              2.866667                  12.3                  6.00  
1              2.866667                  12.3                  6.00  
2              2.866667                  12.3                  6.00  
3              2.950000                  14.8                  6.15  
4              2.950000                  14.8                  6.15  

[5 rows x 24 columns]
In [808]:
df_combined.columns
Out[808]:
Index(['GEOID', 'Perc_pov', 'Perc_BurdRU', 'Perc_BurdHousU', 'Perc_NoVeh',
       'Perc_Crowd', 'HL_rating', 'En_Burden', 'Perc_NoDis', 'Perc_Over65',
       'Perc_Under5', 'Perc_NoEng', 'Perc_UnS', 'Perc_NoIns', 'Perc_Preg12',
       'Data_Value', 'Data_Value_duplicate', 'Data_Value_duplicate',
       'Data_Value_duplicate', 'Data_Value_duplicate', 'Data_Value_duplicate',
       'Data_Value_duplicate', 'Data_Value_duplicate', 'Data_Value_duplicate'],
      dtype='object')
In [809]:
os.getcwd()
Out[809]:
'C:\\Users\\rsingh\\OneDrive - PlanRVA\\Documents\\2_Projects\\Environmental_Analysis\\Heat Sensitivity Index\\Plots\\Final Files'
In [810]:
len(df_combined)
Out[810]:
473
In [811]:
df_combined.columns# Rename the columns
df_combined.columns = [
    'GEOID', 'Perc_pov', 'Perc_BurdRU', 'Perc_BurdHousU', 'Perc_NoVeh',
    'Perc_Crowd', 'HL_rating', 'En_Burden', 'Perc_NoDis', 'Perc_Over65',
    'Perc_Under5', 'Perc_NoEng', 'Perc_UnS', 'Perc_NoIns', 'Perc_Preg12',
    'Asthma', 'Diabetes', 'High_BP', 'Cancer', 'Obesity',
    'Heart', 'Stroke', 'MH', 'COPD'
]

# Display the renamed DataFrame
print(df_combined.head())

         GEOID   Perc_pov  Perc_BurdRU  Perc_BurdHousU  Perc_NoVeh  \
0  51085320602   8.841463     7.612457       13.425606    1.245675   
1  51085320602   8.841463     7.612457       13.425606    1.245675   
2  51085320602   8.841463     7.612457       13.425606    1.245675   
3  51085320601  19.146504    27.843685        5.792045    7.117934   
4  51085320601  19.146504    27.843685        5.792045    7.117934   

   Perc_Crowd  HL_rating  En_Burden  Perc_NoDis  Perc_Over65  ...  \
0    0.276817        2.0        1.0   11.382114    24.457995  ...   
1    0.276817        2.0        1.0   11.382114    24.457995  ...   
2    0.276817        2.0        1.0   11.382114    24.457995  ...   
3    4.396371        2.0        1.0   10.382930    11.830825  ...   
4    4.396371        2.0        1.0   10.382930    11.830825  ...   

   Perc_Preg12     Asthma  Diabetes  High_BP    Cancer    Obesity     Heart  \
0     1.422764   9.033333      10.5    36.30  7.366667  36.733333  6.033333   
1     1.422764   9.033333      10.5    36.30  7.366667  36.733333  6.033333   
2     1.422764   9.033333      10.5    36.30  7.366667  36.733333  6.033333   
3     1.390741  10.000000      10.2    33.65  6.100000  36.900000  5.600000   
4     1.390741  10.000000      10.2    33.65  6.100000  36.900000  5.600000   

     Stroke    MH  COPD  
0  2.866667  12.3  6.00  
1  2.866667  12.3  6.00  
2  2.866667  12.3  6.00  
3  2.950000  14.8  6.15  
4  2.950000  14.8  6.15  

[5 rows x 24 columns]
In [812]:
# Save to Excel
df_combined.to_excel('Heat_Sensitivity_Index_ORIG.xlsx', index=False)

# Display the updated DataFrame with the new index
print(df_combined.head())

         GEOID   Perc_pov  Perc_BurdRU  Perc_BurdHousU  Perc_NoVeh  \
0  51085320602   8.841463     7.612457       13.425606    1.245675   
1  51085320602   8.841463     7.612457       13.425606    1.245675   
2  51085320602   8.841463     7.612457       13.425606    1.245675   
3  51085320601  19.146504    27.843685        5.792045    7.117934   
4  51085320601  19.146504    27.843685        5.792045    7.117934   

   Perc_Crowd  HL_rating  En_Burden  Perc_NoDis  Perc_Over65  ...  \
0    0.276817        2.0        1.0   11.382114    24.457995  ...   
1    0.276817        2.0        1.0   11.382114    24.457995  ...   
2    0.276817        2.0        1.0   11.382114    24.457995  ...   
3    4.396371        2.0        1.0   10.382930    11.830825  ...   
4    4.396371        2.0        1.0   10.382930    11.830825  ...   

   Perc_Preg12     Asthma  Diabetes  High_BP    Cancer    Obesity     Heart  \
0     1.422764   9.033333      10.5    36.30  7.366667  36.733333  6.033333   
1     1.422764   9.033333      10.5    36.30  7.366667  36.733333  6.033333   
2     1.422764   9.033333      10.5    36.30  7.366667  36.733333  6.033333   
3     1.390741  10.000000      10.2    33.65  6.100000  36.900000  5.600000   
4     1.390741  10.000000      10.2    33.65  6.100000  36.900000  5.600000   

     Stroke    MH  COPD  
0  2.866667  12.3  6.00  
1  2.866667  12.3  6.00  
2  2.866667  12.3  6.00  
3  2.950000  14.8  6.15  
4  2.950000  14.8  6.15  

[5 rows x 24 columns]
In [813]:
df_combined.shape
Out[813]:
(473, 24)
In [814]:
# Group by 'GEOID' and take the mean of each group
df_combined = df_combined.groupby('GEOID').mean().reset_index()
In [815]:
df_combined.shape
Out[815]:
(307, 24)
In [816]:
df_hsvi_overall = pd.merge(df_combined,final_df_unique, on ="GEOID", how = "inner")
In [817]:
df_hsvi_overall.shape
Out[817]:
(307, 28)
In [818]:
df_hsvi_overall.columns
Out[818]:
Index(['GEOID', 'Perc_pov', 'Perc_BurdRU', 'Perc_BurdHousU', 'Perc_NoVeh',
       'Perc_Crowd', 'HL_rating', 'En_Burden', 'Perc_NoDis', 'Perc_Over65',
       'Perc_Under5', 'Perc_NoEng', 'Perc_UnS', 'Perc_NoIns', 'Perc_Preg12',
       'Asthma', 'Diabetes', 'High_BP', 'Cancer', 'Obesity', 'Heart', 'Stroke',
       'MH', 'COPD', 'Economic_Index', 'Social_Cultural_Index', 'Health_Index',
       'Heat_Sensitivity_Index'],
      dtype='object')
In [819]:
# Save to Excel
df_hsvi_overall.to_excel('Heat_Sensitivity_Index_FINALFILE.xlsx', index=False)

# Display the updated DataFrame with the new index
print(df_hsvi_overall.head())

         GEOID   Perc_pov  Perc_BurdRU  Perc_BurdHousU  Perc_NoVeh  \
0  51036600100  20.609846     6.015693       18.047079    7.497820   
1  51036600200  15.880371     3.232323       14.646465    6.363636   
2  51036600300  23.076923     7.552083       14.062500    4.817708   
3  51041100106   4.881849     0.000000       13.159747    0.562984   
4  51041100107  40.581442    47.838086        9.383625    2.299908   

   Perc_Crowd  HL_rating  En_Burden  Perc_NoDis  Perc_Over65  ...  Cancer  \
0    0.871840        1.0   1.000000   16.642175    22.520206  ...    6.70   
1    0.202020        1.0   1.000000   19.039596    27.169334  ...    6.70   
2    1.432292        1.0   1.000000   23.377404    28.425481  ...    7.00   
3    0.000000        1.0   0.333333    8.049857    15.684238  ...    7.90   
4    5.427783        1.0   1.000000   14.257268     7.885305  ...    4.85   

   Obesity     Heart    Stroke         MH      COPD  Economic_Index  \
0   41.800  7.500000  4.500000  15.000000  8.700000        0.322675   
1   42.400  7.500000  4.800000  14.800000  8.200000        0.271747   
2   40.800  7.300000  4.500000  13.700000  8.000000        0.316296   
3   30.600  6.133333  2.866667  11.566667  5.566667        0.110661   
4   39.075  5.050000  3.125000  15.525000  5.925000        0.544101   

   Social_Cultural_Index  Health_Index  Heat_Sensitivity_Index  
0               0.462966      0.317886                0.418128  
1               0.563924      0.316531                0.436570  
2               0.583518      0.199188                0.416413  
3               0.186568      0.212030                0.192959  
4               0.646007      0.505472                0.642457  

[5 rows x 28 columns]
In [820]:
len(df_hsvi_overall)
Out[820]:
307
In [821]:
gdf_hsvi_overall = pd.merge(gdf_US_CT, df_hsvi_overall, on = 'GEOID', how = 'inner')
In [ ]:
In [822]:
# Save to Shapefile
gdf_hsvi_overall.to_file('Heat_Sensitivity_Index_OVERALL.shp')

C:\Users\rsingh\AppData\Local\Temp\ipykernel_24884\614432785.py:2: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.
  gdf_hsvi_overall.to_file('Heat_Sensitivity_Index_OVERALL.shp')
In [ ]:
In [ ]: